Methods and apparatus for unsupervised neural replay, learning refinement, association and memory transfer: structural plasticity and structural constraint modeling

ABSTRACT

Certain aspects of the present disclosure support techniques for unsupervised neural replay, learning refinement, association and memory transfer.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application for patent is related by subject matter to U.S.patent application Ser. No. ______, entitled, “METHODS AND APPARATUS FORUNSUPERVISED NEURAL REPLAY, LEARNING REFINEMENT, ASSOCIATION AND MEMORYTRANSFER: NEURAL COMPONENT REPLAY”, filed Nov. 9, 2011, U.S. patentapplication Ser. No. ______, entitled, “METHODS AND APPARATUS FORUNSUPERVISED NEURAL REPLAY, LEARNING REFINEMENT, ASSOCIATION AND MEMORYTRANSFER: NEURAL COMPONENT MEMORY TRANSFER”, filed Nov. 9, 2011 and U.S.patent application Ser. No. ______, entitled, “METHODS AND APPARATUS FORUNSUPERVISED NEURAL REPLAY, LEARNING REFINEMENT, ASSOCIATION AND MEMORYTRANSFER: NEURAL ASSOCIATIVE LEARNING, PATTERN COMPLETION, SEPARATION,GENERALIZATION AND HIERARCHICAL REPLAY”, filed Nov. 9, 2011, filedherewith and assigned to the assignee hereof and hereby expresslyincorporated by reference herein.

BACKGROUND

1. Field

Certain aspects of the present disclosure generally relate to neuralsystem engineering and, more particularly, to methods and apparatus forunsupervised neural replay, learning refinement, association, and memorytransfer.

2. Background

In the field of neural system engineering, there is a fundamentalproblem of truly replaying, in absence of an original stimulus, a neuralfiring pattern that has been learned by one or more neurons. Further,the problems of fast learning, learning refinement, association, andmemory transfer after the original stimulus is no longer present stillremain to be addressed.

Current methods of learning a pattern with biologically inspired neuronmodels are functionally one-way methods: in order to determine whatpattern a neuron matches, one would need to try different patterns untilthe matching one is found. A method of true replay of what has beenlearned, either biologically or by machine is unknown.

SUMMARY

Certain aspects of the present disclosure provide a method of neuralcomponent replay. The method generally includes referencing a pattern ina plurality of afferent neuron outputs with one or more referencingneurons, matching one or more relational aspects between the pattern inthe plurality of afferent neuron outputs and an output of the one ormore referencing neurons with one or more relational aspect neurons, andinducing one or more of the plurality of afferent neurons to output asubstantially similar pattern as the referenced pattern by the one ormore referencing neurons.

Certain aspects of the present disclosure provide an apparatus forneural component replay. The apparatus generally includes a firstcircuit configured to reference a pattern in a plurality of afferentneuron outputs with one or more referencing neurons, a second circuitconfigured to match one or more relational aspects between the patternin the plurality of afferent neuron outputs and an output of the one ormore referencing neurons with one or more relational aspect neurons, anda third circuit configured to induce one or more of the plurality ofafferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons.

Certain aspects of the present disclosure provide an apparatus forneural component replay. The apparatus generally includes means forreferencing a pattern in a plurality of afferent neuron outputs with oneor more referencing neurons, means for matching one or more relationalaspects between the pattern in the plurality of afferent neuron outputsand an output of the one or more referencing neurons with one or morerelational aspect neurons, and means for inducing one or more of theplurality of afferent neurons to output a substantially similar patternas the referenced pattern by the one or more referencing neurons.

Certain aspects of the present disclosure provide a computer programproduct for neural component replay. The computer program productgenerally includes a computer-readable medium comprising code forreferencing a pattern in a plurality of afferent neuron outputs with oneor more referencing neurons, matching one or more relational aspectsbetween the pattern in the plurality of afferent neuron outputs and anoutput of the one or more referencing neurons with one or morerelational aspect neurons, and inducing one or more of the plurality ofafferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons.

Certain aspects of the present disclosure provide a method of neuralcomponent learning refinement and fast learning. The method generallyincludes referencing a pattern in a plurality of afferent neuron outputswith one or more pattern learning neurons, matching one or morerelational aspects between the pattern in the plurality of afferentneuron outputs and an output of one or more referencing neurons with oneor more relational aspect neurons, inducing one or more of the pluralityof afferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons, and refininglearning by the one or more pattern learning neurons using the inducedsubstantially similar pattern.

Certain aspects of the present disclosure provide an apparatus forneural component learning refinement and fast learning. The apparatusgenerally includes a first circuit configured to reference a pattern ina plurality of afferent neuron outputs with one or more pattern learningneurons, a second circuit configured to match one or more relationalaspects between the pattern in the plurality of afferent neuron outputsand an output of one or more referencing neurons with one or morerelational aspect neurons, a third circuit configured to induce one ormore of the plurality of afferent neurons to output a substantiallysimilar pattern as the referenced pattern by the one or more referencingneurons, and a fourth circuit configured to refine learning by the oneor more pattern learning neurons using the induced substantially similarpattern.

Certain aspects of the present disclosure provide an apparatus forneural component learning refinement and fast learning. The apparatusgenerally includes means for referencing a pattern in a plurality ofafferent neuron outputs with one or more pattern learning neurons, meansfor matching one or more relational aspects between the pattern in theplurality of afferent neuron outputs and an output of one or morereferencing neurons with one or more relational aspect neurons, meansfor inducing one or more of the plurality of afferent neurons to outputa substantially similar pattern as the referenced pattern by the one ormore referencing neurons, and means for refining learning by the one ormore pattern learning neurons using the induced substantially similarpattern.

Certain aspects of the present disclosure provide a computer programproduct for neural component learning refinement and fast learning. Thecomputer program product generally includes a computer-readable mediumcomprising code for referencing a pattern in a plurality of afferentneuron outputs with one or more pattern learning neurons, matching oneor more relational aspects between the pattern in the plurality ofafferent neuron outputs and an output of one or more referencing neuronswith one or more relational aspect neurons, inducing one or more of theplurality of afferent neurons to output a substantially similar patternas the referenced pattern by the one or more referencing neurons, andrefining learning by the one or more pattern learning neurons using theinduced substantially similar pattern.

Certain aspects of the present disclosure provide a method of neurallearning refinement. The method generally includes learning a subset ofa pattern in a set of inputs with a stimulus, learning a relationalaspect between elements of the pattern and the subset of the pattern,replaying the pattern in the set of inputs using the learned relationalaspect without the stimulus, and refining learning of the pattern in theset of inputs without the stimulus.

Certain aspects of the present disclosure provide an apparatus forneural learning refinement. The apparatus generally includes a firstcircuit configured to learn a subset of a pattern in a set of inputswith a stimulus, a second circuit configured to learn a relationalaspect between elements of the pattern and the subset of the pattern, athird circuit configured to replay the pattern in the set of inputsusing the learned relational aspect without the stimulus, and a fourthcircuit configured to refine learning of the pattern in the set ofinputs without the stimulus.

Certain aspects of the present disclosure provide an apparatus forneural learning refinement. The apparatus generally includes means forlearning a subset of a pattern in a set of inputs with a stimulus, meansfor learning a relational aspect between elements of the pattern and thesubset of the pattern, means for replaying the pattern in the set ofinputs using the learned relational aspect without the stimulus, andmeans for refining learning of the pattern in the set of inputs withoutthe stimulus.

Certain aspects of the present disclosure provide a computer programproduct for neural learning refinement. The computer program productgenerally includes a computer-readable medium comprising code forlearning a subset of a pattern in a set of inputs with a stimulus,learning a relational aspect between elements of the pattern and thesubset of the pattern, replaying the pattern in the set of inputs usingthe learned relational aspect without the stimulus, and refininglearning of the pattern in the set of inputs without the stimulus.

Certain aspects of the present disclosure provide a method of neuralcomponent replay. The method generally includes referencing a pattern ina plurality of afferent neuron outputs with one or more referencingneurons, matching one or more relational aspects between the pattern inthe plurality of afferent neuron outputs and an output of the one ormore referencing neurons with one or more relational aspect neurons, andinducing one or more of the plurality of afferent neurons to output asubstantially similar pattern as the referenced pattern by the one ormore referencing neurons by bursting the output by the one or morerelational aspect neurons.

Certain aspects of the present disclosure provide an apparatus forneural component replay. The apparatus generally includes a firstcircuit configured to reference a pattern in a plurality of afferentneuron outputs with one or more referencing neurons, a second circuitconfigured to match one or more relational aspects between the patternin the plurality of afferent neuron outputs and an output of the one ormore referencing neurons with one or more relational aspect neurons, anda third circuit configured to induce one or more of the plurality ofafferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons by burstingthe output by the one or more relational aspect neurons.

Certain aspects of the present disclosure provide an apparatus forneural component replay. The apparatus generally includes means forreferencing a pattern in a plurality of afferent neuron outputs with oneor more referencing neurons, means for matching one or more relationalaspects between the pattern in the plurality of afferent neuron outputsand an output of the one or more referencing neurons with one or morerelational aspect neurons, and means for inducing one or more of theplurality of afferent neurons to output a substantially similar patternas the referenced pattern by the one or more referencing neurons bybursting the output by the one or more relational aspect neurons.

Certain aspects of the present disclosure provide a computer programproduct for neural component replay. The computer program productgenerally includes a computer-readable medium comprising code forreferencing a pattern in a plurality of afferent neuron outputs with oneor more referencing neurons, matching one or more relational aspectsbetween the pattern in the plurality of afferent neuron outputs and anoutput of the one or more referencing neurons with one or morerelational aspect neurons, and inducing one or more of the plurality ofafferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons by burstingthe output by the one or more relational aspect neurons.

Certain aspects of the present disclosure provide a method of neuralcomponent replay. The method generally includes referencing a pattern ina plurality of afferent neuron outputs with one or more referencingneurons, matching one or more relational aspects between the pattern inthe plurality of afferent neuron outputs and an output of the one ormore referencing neurons with one or more relational aspect neurons, andinducing one or more of the plurality of afferent neurons to output asubstantially similar pattern as the referenced pattern by the one ormore referencing neurons, wherein signaling between at least one of theafferent neurons, the one or more referencing neurons, or the one ormore relational aspect neurons comprises at least one of a rapid spikesequence or independent spikes.

Certain aspects of the present disclosure provide an apparatus forneural component replay. The apparatus generally includes a firstcircuit configured to reference a pattern in a plurality of afferentneuron outputs with one or more referencing neurons, a second circuitconfigured to match one or more relational aspects between the patternin the plurality of afferent neuron outputs and an output of the one ormore referencing neurons with one or more relational aspect neurons, anda third circuit configured to induce one or more of the plurality ofafferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons, whereinsignaling between at least one of the afferent neurons, the one or morereferencing neurons, or the one or more relational aspect neuronscomprises at least one of a rapid spike sequence or independent spikes.

Certain aspects of the present disclosure provide an apparatus forneural component replay. The apparatus generally includes means forreferencing a pattern in a plurality of afferent neuron outputs with oneor more referencing neurons, means for matching one or more relationalaspects between the pattern in the plurality of afferent neuron outputsand an output of the one or more referencing neurons with one or morerelational aspect neurons, and means for inducing one or more of theplurality of afferent neurons to output a substantially similar patternas the referenced pattern by the one or more referencing neurons,wherein signaling between at least one of the afferent neurons, the oneor more referencing neurons, or the one or more relational aspectneurons comprises at least one of a rapid spike sequence or independentspikes.

Certain aspects of the present disclosure provide a computer programproduct for neural component replay. The computer program productgenerally includes a computer-readable medium comprising code forreferencing a pattern in a plurality of afferent neuron outputs with oneor more referencing neurons, matching one or more relational aspectsbetween the pattern in the plurality of afferent neuron outputs and anoutput of the one or more referencing neurons with one or morerelational aspect neurons, and inducing one or more of the plurality ofafferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons, whereinsignaling between at least one of the afferent neurons, the one or morereferencing neurons, or the one or more relational aspect neuronscomprises at least one of a rapid spike sequence or independent spikes.

Certain aspects of the present disclosure provide a method of neuralcomponent memory transfer. The method generally includes referencing apattern in a plurality of afferent neuron outputs with one or morereferencing neurons, matching one or more first relational aspectsbetween the pattern in the plurality of afferent neuron outputs and anoutput of the one or more referencing neurons with one or more firstrelational aspect neurons, and transferring the pattern to one or moretransferee neurons by inducing the plurality of afferent neurons tooutput a first substantially similar pattern as the referenced patternby the one or more referencing neurons.

Certain aspects of the present disclosure provide an apparatus forneural component memory transfer. The apparatus generally includes afirst circuit configured to reference a pattern in a plurality ofafferent neuron outputs with one or more referencing neurons, a secondcircuit configured to match one or more first relational aspects betweenthe pattern in the plurality of afferent neuron outputs and an output ofthe one or more referencing neurons with one or more first relationalaspect neurons, and a third circuit configured to transfer the patternto one or more transferee neurons by inducing the plurality of afferentneurons to output a first substantially similar pattern as thereferenced pattern by the one or more referencing neurons.

Certain aspects of the present disclosure provide an apparatus forneural component memory transfer. The apparatus generally includes meansfor referencing a pattern in a plurality of afferent neuron outputs withone or more referencing neurons, means for matching one or more firstrelational aspects between the pattern in the plurality of afferentneuron outputs and an output of the one or more referencing neurons withone or more first relational aspect neurons, and means for transferringthe pattern to one or more transferee neurons by inducing the pluralityof afferent neurons to output a first substantially similar pattern asthe referenced pattern by the one or more referencing neurons.

Certain aspects of the present disclosure provide a computer programproduct for neural component memory transfer. The computer programproduct generally includes a computer-readable medium comprising codefor referencing a pattern in a plurality of afferent neuron outputs withone or more referencing neurons, matching one or more first relationalaspects between the pattern in the plurality of afferent neuron outputsand an output of the one or more referencing neurons with one or morefirst relational aspect neurons, and transferring the pattern to one ormore transferee neurons by inducing the plurality of afferent neurons tooutput a first substantially similar pattern as the referenced patternby the one or more referencing neurons.

Certain aspects of the present disclosure provide a method of neuralassociative learning. The method generally includes referencing a firstpattern in a set of one or more inputs with a first stimulus, learning arelational aspect between one or more elements of the first pattern inthe set of inputs and referencing of the first pattern, referencing asecond pattern in the set of one or more inputs with a second stimulus,learning a relational aspect between one or more elements of the secondpattern in the set of inputs and referencing of the second pattern,replaying the first and second patterns in the set of inputs using thelearned relational aspects without the first and second stimuli, andassociating the first and second patterns based on the replay.

Certain aspects of the present disclosure provide an apparatus forneural associative learning. The apparatus generally includes a firstcircuit configured to reference a first pattern in a set of one or moreinputs with a first stimulus, a second circuit configured to learn arelational aspect between one or more elements of the first pattern inthe set of inputs and referencing of the first pattern, a third circuitconfigured to reference a second pattern in the set of one or moreinputs with a second stimulus, a fourth circuit configured to learn arelational aspect between one or more elements of the second pattern inthe set of inputs and referencing of the second pattern, a fifth circuitconfigured to replay the first and second patterns in the set of inputsusing the learned relational aspects without the first and secondstimuli, and a sixth circuit configured to associate the first andsecond patterns based on the replay.

Certain aspects of the present disclosure provide an apparatus forneural associative learning. The apparatus generally includes means forreferencing a first pattern in a set of one or more inputs with a firststimulus, means for learning a relational aspect between one or moreelements of the first pattern in the set of inputs and referencing ofthe first pattern, means for referencing a second pattern in the set ofone or more inputs with a second stimulus, means for learning arelational aspect between one or more elements of the second pattern inthe set of inputs and referencing of the second pattern, means forreplaying the first and second patterns in the set of inputs using thelearned relational aspects without the first and second stimuli, andmeans for associating the first and second patterns based on the replay.

Certain aspects of the present disclosure provide a computer programproduct for neural associative learning. The computer program productgenerally includes a computer-readable medium comprising code forreferencing a first pattern in a set of one or more inputs with a firststimulus, learning a relational aspect between one or more elements ofthe first pattern in the set of inputs and referencing of the firstpattern, referencing a second pattern in the set of one or more inputswith a second stimulus, learning a relational aspect between one or moreelements of the second pattern in the set of inputs and referencing ofthe second pattern, replaying the first and second patterns in the setof inputs using the learned relational aspects without the first andsecond stimuli, and associating the first and second patterns based onthe replay.

Certain aspects of the present disclosure provide a method of neuralcomparison. The method generally includes referencing a first pattern ina set of one or more inputs with a first stimulus, learning a relationalaspect between one or more elements of the first pattern in the set ofinputs and referencing of the first pattern, referencing a secondpattern in the set of one or more inputs with a second stimulus,replaying the first pattern, and comparing the first pattern with thesecond pattern based on the replay and referencing of the first andsecond patterns.

Certain aspects of the present disclosure provide an apparatus forneural comparison. The apparatus generally includes a first circuitconfigured to reference a first pattern in a set of one or more inputswith a first stimulus, a second circuit configured to learn a relationalaspect between one or more elements of the first pattern in the set ofinputs and referencing of the first pattern, a third circuit configuredto reference a second pattern in the set of one or more inputs with asecond stimulus, a fourth circuit configured to replay the firstpattern, and a fifth circuit configured to compare the first patternwith the second pattern based on the replay and referencing of the firstand second patterns.

Certain aspects of the present disclosure provide an apparatus forneural comparison. The apparatus generally includes means forreferencing a first pattern in a set of one or more inputs with a firststimulus, means for learning a relational aspect between one or moreelements of the first pattern in the set of inputs and referencing ofthe first pattern, means for referencing a second pattern in the set ofone or more inputs with a second stimulus, means for replaying the firstpattern, and means for comparing the first pattern with the secondpattern based on the replay and referencing of the first and secondpatterns.

Certain aspects of the present disclosure provide a computer programproduct for neural comparison. The computer program product generallyincludes a computer-readable medium comprising code for referencing afirst pattern in a set of one or more inputs with a first stimulus,learning a relational aspect between one or more elements of the firstpattern in the set of inputs and referencing of the first pattern,referencing a second pattern in the set of one or more inputs with asecond stimulus, replaying the first pattern, and comparing the firstpattern with the second pattern based on the replay and referencing ofthe first and second patterns.

Certain aspects of the present disclosure provide a method of neuralpattern completion. The method generally includes referencing a firstpattern in a set of one or more inputs with a first stimulus, learning arelational aspect between one or more elements of the first pattern inthe set of inputs and referencing of first pattern, referencing a secondpattern in the set of one or more inputs with a second stimulus, whereinthe second pattern comprises a degraded version of the first pattern,and replaying at least one element of the first pattern missing or beingdegraded from the second pattern in response to exposure to at least oneof the second pattern or the second stimulus.

Certain aspects of the present disclosure provide an apparatus forneural pattern completion. The apparatus generally includes a firstcircuit configured to reference a first pattern in a set of one or moreinputs with a first stimulus, a second circuit configured to learn arelational aspect between one or more elements of the first pattern inthe set of inputs and referencing of first pattern, a third circuitconfigured to reference a second pattern in the set of one or moreinputs with a second stimulus, wherein the second pattern comprises adegraded version of the first pattern, and a fourth circuit configuredto replay at least one element of the first pattern missing or beingdegraded from the second pattern in response to exposure to at least oneof the second pattern or the second stimulus.

Certain aspects of the present disclosure provide an apparatus forneural pattern completion. The apparatus generally includes means forreferencing a first pattern in a set of one or more inputs with a firststimulus, means for learning a relational aspect between one or moreelements of the first pattern in the set of inputs and referencing offirst pattern, means for referencing a second pattern in the set of oneor more inputs with a second stimulus, wherein the second patterncomprises a degraded version of the first pattern, and means forreplaying at least one element of the first pattern missing or beingdegraded from the second pattern in response to exposure to at least oneof the second pattern or the second stimulus.

Certain aspects of the present disclosure provide a computer programproduct for neural pattern completion. The computer program productgenerally includes a computer-readable medium comprising code forreferencing a first pattern in a set of one or more inputs with a firststimulus, learning a relational aspect between one or more elements ofthe first pattern in the set of inputs and referencing of first pattern,referencing a second pattern in the set of one or more inputs with asecond stimulus, wherein the second pattern comprises a degraded versionof the first pattern, and replaying at least one element of the firstpattern missing or being degraded from the second pattern in response toexposure to at least one of the second pattern or the second stimulus.

Certain aspects of the present disclosure provide a method of neuralpattern separation. The method generally includes referencing a firstpattern in a set of one or more inputs with one or more referencingneurons, learning a first relational aspect between one or more elementsof the first pattern and referencing of the first pattern, referencing asecond pattern in the set of one or more inputs with the one or morereferencing neurons, wherein the second pattern is similar to the firstpattern, learning a second relational aspect between one or moreelements of the second pattern and referencing of the second pattern,modifying at least one of the first pattern or the second pattern toincrease a difference between the first and second patterns, andreferencing, after the modification using the one or more referencingneurons, the first pattern with a first stimulus and the second patternwith a second stimulus, wherein the first stimulus is distinct from thesecond stimulus.

Certain aspects of the present disclosure provide an apparatus forneural pattern separation. The apparatus generally includes a firstcircuit configured to reference a first pattern in a set of one or moreinputs with one or more referencing neurons, a second circuit configuredto learn a first relational aspect between one or more elements of thefirst pattern and referencing of the first pattern, a third circuitconfigured to reference a second pattern in the set of one or moreinputs with the one or more referencing neurons, wherein the secondpattern is similar to the first pattern, a fourth circuit configured tolearn a second relational aspect between one or more elements of thesecond pattern and referencing of the second pattern, a fifth circuitconfigured to modify at least one of the first pattern or the secondpattern to increase a difference between the first and second patterns,and a sixth circuit configured to reference, after the modificationusing the one or more referencing neurons, the first pattern with afirst stimulus and the second pattern with a second stimulus, whereinthe first stimulus is distinct from the second stimulus.

Certain aspects of the present disclosure provide an apparatus forneural pattern separation. The apparatus generally includes means forreferencing a first pattern in a set of one or more inputs with one ormore referencing neurons, means for learning a first relational aspectbetween one or more elements of the first pattern and referencing of thefirst pattern, means for referencing a second pattern in the set of oneor more inputs with the one or more referencing neurons, wherein thesecond pattern is similar to the first pattern, means for learning asecond relational aspect between one or more elements of the secondpattern and referencing of the second pattern, means for modifying atleast one of the first pattern or the second pattern to increase adifference between the first and second patterns, and means forreferencing, after the modification using the one or more referencingneurons, the first pattern with a first stimulus and the second patternwith a second stimulus, wherein the first stimulus is distinct from thesecond stimulus.

Certain aspects of the present disclosure provide a computer programproduct for neural pattern separation. The computer program productgenerally includes a computer-readable medium comprising code forreferencing a first pattern in a set of one or more inputs with one ormore referencing neurons, learning a first relational aspect between oneor more elements of the first pattern and referencing of the firstpattern, referencing a second pattern in the set of one or more inputswith the one or more referencing neurons, wherein the second pattern issimilar to the first pattern, learning a second relational aspectbetween one or more elements of the second pattern and referencing ofthe second pattern, modifying at least one of the first pattern or thesecond pattern to increase a difference between the first and secondpatterns, and referencing, after the modification using the one or morereferencing neurons, the first pattern with a first stimulus and thesecond pattern with a second stimulus, wherein the first stimulus isdistinct from the second stimulus.

Certain aspects of the present disclosure provide a method of neuralpattern generalization. The method generally includes referencing afirst pattern in a set of one or more inputs with a first stimulus,learning a relational aspect between one or more elements of the firstpattern in the set of inputs and referencing of the first pattern,referencing a second pattern in the set of one or more inputs with asecond stimulus, learning a relational aspect between one or moreelements of the second pattern in the set of inputs and referencing ofthe second pattern, replaying at least one of the first pattern or thesecond pattern without the first and second stimuli, and learning ageneralization of the first and second patterns based on the replay.

Certain aspects of the present disclosure provide an apparatus forneural pattern generalization. The apparatus generally includes a firstcircuit configured to reference a first pattern in a set of one or moreinputs with a first stimulus, a second circuit configured to learn arelational aspect between one or more elements of the first pattern inthe set of inputs and referencing of the first pattern, a third circuitconfigured to reference a second pattern in the set of one or moreinputs with a second stimulus, a fourth circuit configured to learn arelational aspect between one or more elements of the second pattern inthe set of inputs and referencing of the second pattern, a fifth circuitconfigured to replay at least one of the first pattern or the secondpattern without the first and second stimuli, and a sixth circuitconfigured to learn a generalization of the first and second patternsbased on the replay.

Certain aspects of the present disclosure provide an apparatus forneural pattern generalization. The apparatus generally includes meansfor referencing a first pattern in a set of one or more inputs with afirst stimulus, means for learning a relational aspect between one ormore elements of the first pattern in the set of inputs and referencingof the first pattern, means for referencing a second pattern in the setof one or more inputs with a second stimulus, means for learning arelational aspect between one or more elements of the second pattern inthe set of inputs and referencing of the second pattern, means forreplaying at least one of the first pattern or the second patternwithout the first and second stimuli, and means for learning ageneralization of the first and second patterns based on the replay.

Certain aspects of the present disclosure provide a computer programproduct for neural pattern generalization. The computer program productgenerally includes a computer-readable medium comprising code forreferencing a first pattern in a set of one or more inputs with a firststimulus, learning a relational aspect between one or more elements ofthe first pattern in the set of inputs and referencing of the firstpattern, referencing a second pattern in the set of one or more inputswith a second stimulus, learning a relational aspect between one or moreelements of the second pattern in the set of inputs and referencing ofthe second pattern, replaying at least one of the first pattern or thesecond pattern without the first and second stimuli, and learning ageneralization of the first and second patterns based on the replay.

Certain aspects of the present disclosure provide a method of neuralpattern sequence completion. The method generally includes referencingeach sequence of parts of a pattern in a set of one or more first layerneurons with a second layer of referencing neurons, learning arelational aspect between one or more elements of the pattern and thereferencing of that sequence of parts of the pattern, referencing apattern sequence in the second layer of referencing neurons with a thirdlayer of referencing neurons, learning a relational aspect between oneor more elements of the pattern sequence and the referencing of patternsequence in the second layer of referencing neurons, and replaying asubsequent part of the pattern in the first layer neurons upon producinga prior part of the pattern.

Certain aspects of the present disclosure provide an apparatus forneural pattern sequence completion. The apparatus generally includes afirst circuit configured to reference each sequence of parts of apattern in a set of one or more first layer neurons with a second layerof referencing neurons, a second circuit configured to learn arelational aspect between one or more elements of the pattern and thereferencing of that sequence of parts of the pattern, a third circuitconfigured to reference a pattern sequence in the second layer ofreferencing neurons with a third layer of referencing neurons, a fourthcircuit configured to learn a relational aspect between one or moreelements of the pattern sequence and the referencing of pattern sequencein the second layer of referencing neurons, and a fifth circuitconfigured to replay a subsequent part of the pattern in the first layerneurons upon producing a prior part of the pattern.

Certain aspects of the present disclosure provide an apparatus forneural pattern sequence completion. The apparatus generally includesmeans for referencing each sequence of parts of a pattern in a set ofone or more first layer neurons with a second layer of referencingneurons, means for learning a relational aspect between one or moreelements of the pattern and the referencing of that sequence of parts ofthe pattern, means for referencing a pattern sequence in the secondlayer of referencing neurons with a third layer of referencing neurons,means for learning a relational aspect between one or more elements ofthe pattern sequence and the referencing of pattern sequence in thesecond layer of referencing neurons, and means for replaying asubsequent part of the pattern in the first layer neurons upon producinga prior part of the pattern.

Certain aspects of the present disclosure provide a computer programproduct for neural pattern sequence completion. The computer programproduct generally includes a computer-readable medium comprising codefor referencing each sequence of parts of a pattern in a set of one ormore first layer neurons with a second layer of referencing neurons,learning a relational aspect between one or more elements of the patternand the referencing of that sequence of parts of the pattern,referencing a pattern sequence in the second layer of referencingneurons with a third layer of referencing neurons, learning a relationalaspect between one or more elements of the pattern sequence and thereferencing of pattern sequence in the second layer of referencingneurons, and replaying a subsequent part of the pattern in the firstlayer neurons upon producing a prior part of the pattern.

Certain aspects of the present disclosure provide a method of neuralpattern hierarchical replay. The method generally includes referencingeach sequence of parts of a pattern in a set of one or more first layerneurons with a second layer of referencing neurons, learning arelational aspect between one or more elements of each pattern and thereferencing of that sequence of parts of the pattern in the set of oneor more first layer neurons, referencing a pattern sequence in thesecond layer of referencing neurons with a third layer of referencingneurons, learning a relational aspect between one or more elements ofthe pattern sequence and the referencing of the pattern sequence in thesecond layer of referencing neurons, invoking replay of the referencingof the pattern sequence in the second layer based on the third layer ofreferencing neurons, and replaying that sequence of parts of the patternin the first layer based on the invoking of replay of the referencing ofthe pattern sequence in the second layer.

Certain aspects of the present disclosure provide an apparatus forneural pattern hierarchical replay. The apparatus generally includes afirst circuit configured to reference each sequence of parts of apattern in a set of one or more first layer neurons with a second layerof referencing neurons, a second circuit configured to learn arelational aspect between one or more elements of each pattern and thereferencing of that sequence of parts of the pattern, a third circuitconfigured to reference a pattern sequence in the second layer ofreferencing neurons with a third layer of referencing neurons, a fourthcircuit configured to learn a relational aspect between one or moreelements of the pattern sequence and the referencing of the patternsequence in the second layer of referencing neurons, a fifth circuitconfigured to invoke replay of the referencing of the pattern sequencein the second layer based on the third layer of referencing neurons, anda sixth circuit configured to replay that sequence of parts of thepattern in the first layer based on the invoking of replay of thereferencing of the pattern sequence in the second layer.

Certain aspects of the present disclosure provide an apparatus forneural pattern hierarchical replay. The apparatus generally includesmeans for referencing each sequence of parts of a pattern in a set ofone or more first layer neurons with a second layer of referencingneurons, means for learning a relational aspect between one or moreelements of each pattern and the referencing of that sequence of partsof the pattern, means for referencing a pattern sequence in the secondlayer of referencing neurons with a third layer of referencing neurons,means for learning a relational aspect between one or more elements ofthe pattern sequence and the referencing of the pattern sequence in thesecond layer of referencing neurons, means for invoking replay of thereferencing of the pattern sequence in the second layer based on thethird layer of referencing neurons, and means for replaying thatsequence of parts of the pattern in the first layer based on theinvoking of replay of the referencing of the pattern sequence in thesecond layer.

Certain aspects of the present disclosure provide a computer programproduct for neural pattern hierarchical replay. The computer programproduct generally includes a computer-readable medium comprising codefor referencing each sequence of parts of a pattern in a set of one ormore first layer neurons with a second layer of referencing neurons,learning a relational aspect between one or more elements of eachpattern and the referencing of that sequence of parts of the pattern inthe set of one or more first layer neurons, referencing a patternsequence in the second layer of referencing neurons with a third layerof referencing neurons, learning a relational aspect between one or moreelements of the pattern sequence and the referencing of the patternsequence in the second layer of referencing neurons, invoking replay ofthe referencing of the pattern sequence in the second layer based on thethird layer of referencing neurons, and replaying that sequence of partsof the pattern in the first layer based on the invoking of replay of thereferencing of the pattern sequence in the second layer.

Certain aspects of the present disclosure provide a method of neuralpattern sequence completion. The method generally includes referencing aplurality of parts of a pattern in a plurality of afferent neurons witha plurality of referencing neurons, relating, with one or morerelational aspect neurons, one or more of the parts of the pattern to asubset of the referencing neurons based on a delay between the afferentneurons and the one or more relational aspect neurons being smaller thana first value, relating, with the one or more relational aspect neurons,one or more remaining parts of the pattern to the subset of referencingneurons based on the delay being larger than a second value, andinducing replay of the one or more remaining parts of the pattern by thesubset of referencing neurons based on firing elements of the one ormore parts of the pattern by the afferent neurons.

Certain aspects of the present disclosure provide an apparatus forneural pattern sequence completion. The apparatus generally includes afirst circuit configured to reference a plurality of parts of a patternin a plurality of afferent neurons with a plurality of referencingneurons, a second circuit configured to relate, with one or morerelational aspect neurons, one or more of the parts of the pattern to asubset of the referencing neurons based on a delay between the afferentneurons and the one or more relational aspect neurons being smaller thana first value, a third circuit configured to relate, with the one ormore relational aspect neurons, one or more remaining parts of thepattern to the subset of referencing neurons based on the delay beinglarger than a second value, and a fourth circuit configured to inducereplay of the one or more remaining parts of the pattern by the subsetof referencing neurons based on firing elements of the one or more partsof the pattern by the afferent neurons.

Certain aspects of the present disclosure provide an apparatus forneural pattern sequence completion. The apparatus generally includesmeans for referencing a plurality of parts of a pattern in a pluralityof afferent neurons with a plurality of referencing neurons, means forrelating, with one or more relational aspect neurons, one or more of theparts of the pattern to a subset of the referencing neurons based on adelay between the afferent neurons and the one or more relational aspectneurons being smaller than a first value, means for relating, with theone or more relational aspect neurons, one or more remaining parts ofthe pattern to the subset of referencing neurons based on the delaybeing larger than a second value, and means for inducing replay of theone or more remaining parts of the pattern by the subset of referencingneurons based on firing elements of the one or more parts of the patternby the afferent neurons.

Certain aspects of the present disclosure provide a computer programproduct for neural pattern sequence completion. The computer programproduct generally includes a computer-readable medium comprising codefor referencing a plurality of parts of a pattern in a plurality ofafferent neurons with a plurality of referencing neurons, relating, withone or more relational aspect neurons, one or more of the parts of thepattern to a subset of the referencing neurons based on a delay betweenthe afferent neurons and the one or more relational aspect neurons beingsmaller than a first value, relating, with the one or more relationalaspect neurons, one or more remaining parts of the pattern to the subsetof referencing neurons based on the delay being larger than a secondvalue, and inducing replay of the one or more remaining parts of thepattern by the subset of referencing neurons based on firing elements ofthe one or more parts of the pattern by the afferent neurons.

Certain aspects of the present disclosure provide a method of neuralcomponent replay. The method generally includes referencing a pattern ina plurality of afferent neuron outputs with one or more referencingneurons, learning one or more relational aspects between the pattern inthe plurality of afferent neuron outputs and an output of the one ormore referencing neurons with one or more relational aspect neuronsusing structural plasticity, and inducing one or more of the pluralityof afferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons.

Certain aspects of the present disclosure provide an apparatus forneural component replay. The apparatus generally includes a firstcircuit configured to reference a pattern in a plurality of afferentneuron outputs with one or more referencing neurons, a second circuitconfigured to learn one or more relational aspects between the patternin the plurality of afferent neuron outputs and an output of the one ormore referencing neurons with one or more relational aspect neuronsusing structural plasticity, and a third circuit configured to induceone or more of the plurality of afferent neurons to output asubstantially similar pattern as the referenced pattern by the one ormore referencing neurons.

Certain aspects of the present disclosure provide an apparatus forneural component replay. The apparatus generally includes means forreferencing a pattern in a plurality of afferent neuron outputs with oneor more referencing neurons, means for learning one or more relationalaspects between the pattern in the plurality of afferent neuron outputsand an output of the one or more referencing neurons with one or morerelational aspect neurons using structural plasticity, and means forinducing one or more of the plurality of afferent neurons to output asubstantially similar pattern as the referenced pattern by the one ormore referencing neurons.

Certain aspects of the present disclosure provide a computer programproduct for neural component replay. The computer program productgenerally includes a computer-readable medium comprising code forreferencing a pattern in a plurality of afferent neuron outputs with oneor more referencing neurons, learning one or more relational aspectsbetween the pattern in the plurality of afferent neuron outputs and anoutput of the one or more referencing neurons with one or morerelational aspect neurons using structural plasticity, and inducing oneor more of the plurality of afferent neurons to output a substantiallysimilar pattern as the referenced pattern by the one or more referencingneurons.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above-recited features of the presentdisclosure can be understood in detail, a more particular description,briefly summarized above, may be had by reference to aspects, some ofwhich are illustrated in the appended drawings. It is to be noted,however, that the appended drawings illustrate only certain typicalaspects of this disclosure and are therefore not to be consideredlimiting of its scope, for the description may admit to other equallyeffective aspects.

FIG. 1 illustrates an example network of neurons in accordance withcertain aspects of the present disclosure.

FIG. 2 illustrates an example of afferent neurons connected to apattern-matching neuron in accordance with certain aspects of thepresent disclosure.

FIG. 3 illustrates an example of general method of component replay inaccordance with certain aspects of the present disclosure.

FIG. 4 illustrates an example of demonstrative replay embodiment inaccordance with certain aspects of the present disclosure.

FIG. 5 illustrates another example of demonstrative replay embodiment inaccordance with certain aspects of the present disclosure.

FIG. 6 illustrates an example of preferred replay embodiment inaccordance with certain aspects of the present disclosure.

FIG. 7 illustrates an example of pattern matching and replaying inaccordance with certain aspects of the present disclosure.

FIG. 8 illustrates example operations for neural component replay inaccordance with certain aspects of the present disclosure.

FIG. 8A illustrates example components capable of performing theoperations illustrated in FIG. 8.

FIG. 9 illustrates an example model of preferred replay embodiment inaccordance with certain aspects of the present disclosure.

FIG. 10 illustrates another example model of preferred replay embodimentin accordance with certain aspects of the present disclosure.

FIG. 11 illustrates an example diagram of pattern learning andrelational learning in accordance with certain aspects of the presentdisclosure.

FIG. 12 illustrates an example of synaptic weights obtained based onstructural plasticity learning rule in accordance with certain aspectsof the present disclosure.

FIG. 13 illustrates example graphs of spike-timing-dependent plasticity(STDP) learning rule in accordance with certain aspects of the presentdisclosure.

FIG. 14 illustrates an example graph of modified STDP learning rule inaccordance with certain aspects of the present disclosure.

FIG. 15 illustrates an example diagram of feedback of relational firingback to afferents in accordance with certain aspects of the presentdisclosure.

FIG. 16 illustrates example operations for neural component replay inrelation to structural plasticity and structural constraint modeling inaccordance with certain aspects of the present disclosure.

FIG. 16A illustrates example components capable of performing theoperations illustrated in FIG. 16.

FIG. 17 illustrates an example network diagram for controlling replay inaccordance with certain aspects of the present disclosure.

FIG. 18 illustrates an example of excitatory oscillation in accordancewith certain aspects of the present disclosure.

FIG. 19 illustrates an example of controlling replay with bursting inaccordance with certain aspects of the present disclosure.

FIG. 20 illustrates another example of controlling replay with burstingin accordance with certain aspects of the present disclosure.

FIG. 21 illustrates an example model of afferent, reference neuron andrelational-aspect-learning neuron in accordance with certain aspects ofthe present disclosure.

FIG. 22 illustrates an example diagram of afferent, reference neuron andrelational-aspect-learning neuron in accordance with certain aspects ofthe present disclosure.

FIG. 23 illustrates other example operations for neural component replayin accordance with certain aspects of the present disclosure.

FIG. 23A illustrates example components capable of performing theoperations illustrated in FIG. 23.

FIG. 24 illustrates other example operations for neural component replayin accordance with certain aspects of the present disclosure.

FIG. 24A illustrates example components capable of performing theoperations illustrated in FIG. 24.

FIG. 25 illustrates an example replay of multiple patterns in accordancewith certain aspects of the present disclosure.

FIG. 26 illustrates examples of flawed and useful learning refinementconcepts in accordance with certain aspects of the present disclosure.

FIG. 27 illustrates examples of relational-aspect learning neurons inaccordance with certain aspects of the present disclosure.

FIG. 28 illustrates an example of learning refinement in accordance withcertain aspects of the present disclosure.

FIG. 29 illustrates example operations for neural learning refinement inaccordance with certain aspects of the present disclosure.

FIG. 29A illustrates example components capable of performing theoperations illustrated in FIG. 29.

FIG. 30 illustrates examples of flawed and useful memory transferconcepts in accordance with certain aspects of the present disclosure.

FIG. 31 illustrates an example of network of neurons for memory transferin accordance with certain aspects of the present disclosure.

FIG. 32 illustrates an example of connectivity between neurons formemory transfer in accordance with certain aspects of the presentdisclosure.

FIG. 33 illustrates another example of connectivity between neurons formemory transfer in accordance with certain aspects of the presentdisclosure.

FIG. 34 illustrates an example of connectivity between neurons formemory transfer and association in accordance with certain aspects ofthe present disclosure.

FIG. 35 illustrates example operations for neural component memorytransfer in accordance with certain aspects of the present disclosure.

FIG. 35A illustrates example components capable of performing theoperations illustrated in FIG. 35.

FIG. 36 illustrates example operations for neural associative learningin accordance with certain aspects of the present disclosure.

FIG. 36A illustrates example components capable of performing theoperations illustrated in FIG. 36.

FIG. 37 illustrates an example of pattern completion in accordance withcertain aspects of the present disclosure.

FIG. 38 illustrates an example of degraded input with lagging completionin accordance with certain aspects of the present disclosure.

FIG. 39 illustrates an example of degraded input with replay completionin accordance with certain aspects of the present disclosure.

FIG. 40 illustrates an example of degraded input with late completion inaccordance with certain aspects of the present disclosure.

FIG. 41 illustrates example operations for neural pattern completion inaccordance with certain aspects of the present disclosure.

FIG. 41A illustrates example components capable of performing theoperations illustrated in FIG. 41.

FIG. 42 illustrates example operations for neural pattern separation inaccordance with certain aspects of the present disclosure.

FIG. 42A illustrates example components capable of performing theoperations illustrated in FIG. 42.

FIG. 43 illustrates an example of pattern comparison in accordance withcertain aspects of the present disclosure.

FIG. 44 illustrates example operations for neural comparison inaccordance with certain aspects of the present disclosure.

FIG. 44A illustrates example components capable of performing theoperations illustrated in FIG. 44.

FIG. 45 illustrates example operations for neural pattern generalizationin accordance with certain aspects of the present disclosure.

FIG. 45A illustrates example components capable of performing theoperations illustrated in FIG. 45.

FIG. 46 illustrates an example of horizontal association of neurons inaccordance with certain aspects of the present disclosure.

FIG. 47 illustrates an example of pattern learning with referencing inaccordance with certain aspects of the present disclosure.

FIG. 48 illustrates example operations for neural component learningrefinement and fast learning in accordance with certain aspects of thepresent disclosure.

FIG. 48A illustrates example components capable of performing theoperations illustrated in FIG. 48.

FIG. 49 illustrates an example of procedural flow, repeating replay anddirecting flow in accordance with certain aspects of the presentdisclosure.

FIG. 50 illustrates an example of hierarchical pattern replay inaccordance with certain aspects of the present disclosure.

FIG. 51 illustrates an example block diagram of pattern completion inaccordance with certain aspects of the present disclosure.

FIG. 52 illustrates example operations for neural pattern sequencecompletion in accordance with certain aspects of the present disclosure.

FIG. 52A illustrates example components capable of performing theoperations illustrated in FIG. 52.

FIG. 53 illustrates example operations for neural pattern hierarchicalreplay in accordance with certain aspects of the present disclosure.

FIG. 53A illustrates example components capable of performing theoperations illustrated in FIG. 53.

FIG. 54 illustrates example operations for neural pattern sequencecompletion that may be performed without a hierarchy in accordance withcertain aspects of the present disclosure.

FIG. 54A illustrates example components capable of performing theoperations illustrated in FIG. 54.

FIG. 55 illustrates an example software implementation of neuralcomponent replay, learning refinement, memory transfer, associativelearning, pattern comparison, pattern completion, pattern separation,pattern generalization, pattern sequence completion with a hierarchy,and pattern hierarchical replay using a general-purpose processor inaccordance with certain aspects of the present disclosure.

FIG. 56 illustrates an example implementation of neural componentreplay, learning refinement, memory transfer, associative learning,pattern comparison, pattern completion, pattern separation, patterngeneralization, pattern sequence completion with a hierarchy, andpattern hierarchical replay where a memory may be interfaced withindividual distributed processing units in accordance with certainaspects of the present disclosure.

FIG. 57 illustrates an example implementation of neural componentreplay, learning refinement, memory transfer, associative learning,pattern comparison, pattern completion, pattern separation, patterngeneralization, pattern sequence completion with a hierarchy, andpattern hierarchical replay based on distributed memories anddistributed processing units in accordance with certain aspects of thepresent disclosure.

DETAILED DESCRIPTION

Various aspects of the disclosure are described more fully hereinafterwith reference to the accompanying drawings. This disclosure may,however, be embodied in many different forms and should not be construedas limited to any specific structure or function presented throughoutthis disclosure. Rather, these aspects are provided so that thisdisclosure will be thorough and complete, and will fully convey thescope of the disclosure to those skilled in the art. Based on theteachings herein one skilled in the art should appreciate that the scopeof the disclosure is intended to cover any aspect of the disclosuredisclosed herein, whether implemented independently of or combined withany other aspect of the disclosure. For example, an apparatus may beimplemented or a method may be practiced using any number of the aspectsset forth herein. In addition, the scope of the disclosure is intendedto cover such an apparatus or method which is practiced using otherstructure, functionality, or structure and functionality in addition toor other than the various aspects of the disclosure set forth herein. Itshould be understood that any aspect of the disclosure disclosed hereinmay be embodied by one or more elements of a claim.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the disclosure.Although some benefits and advantages of the preferred aspects arementioned, the scope of the disclosure is not intended to be limited toparticular benefits, uses or objectives. Rather, aspects of thedisclosure are intended to be broadly applicable to differenttechnologies, system configurations, networks and protocols, some ofwhich are illustrated by way of example in the figures and in thefollowing description of the preferred aspects. The detailed descriptionand drawings are merely illustrative of the disclosure rather thanlimiting, the scope of the disclosure being defined by the appendedclaims and equivalents thereof.

An Example Neural System

FIG. 1 illustrates an example neural system 100 with multiple levels ofneurons in accordance with certain aspects of the present disclosure.The neural system 100 may comprise a level of neurons 102 connected toanother level of neurons 106 though a network of synaptic connections104. For simplicity, only two levels of neurons are illustrated in FIG.1, although fewer or more levels of neurons may exist in a typicalneural system.

As illustrated in FIG. 1, each neuron in the level 102 may receive aninput signal 108 that may be generated by a plurality of neurons of aprevious level (not shown in FIG. 1). The signal 108 may represent aninput current of the level 102 neuron. This current may be accumulatedon the neuron membrane to charge a membrane potential. When the membranepotential reaches its threshold value, the neuron may fire and generatean output spike to be transferred to the next level of neurons (e.g.,the level 106).

The transfer of spikes from one level of neurons to another may beachieved through the network of synaptic connections (or simply“synapses”) 104, as illustrated in FIG. 1. The synapses 104 may receiveoutput signals (i.e., spikes) from the level 102 neurons, scale thosesignals according to adjustable synaptic weights w₁ ^((i,i+1)), . . . ,w_(P) ^((i,i+1)) (where P is a total number of synaptic connectionsbetween the neurons of levels 102 and 106), and combine the scaledsignals as an input signal of each neuron in the level 106. Every neuronin the level 106 may generate output spikes 110 based on thecorresponding combined input signal. The output spikes 110 may be thentransferred to another level of neurons using another network ofsynaptic connections (not shown in FIG. 1).

The neural system 100 may be emulated by an electrical circuit andutilized in a large range of applications, such as image and patternrecognition, machine learning, motor control, and alike. Each neuron inthe neural system 100 may be implemented as a neuron circuit. The neuronmembrane charged to the threshold value initiating the output spike maybe implemented, for example, as a capacitor that integrates anelectrical current flowing through it.

In an aspect, the capacitor may be eliminated as the electrical currentintegrating device of the neuron circuit, and a smaller memristorelement may be used in its place. This approach may be applied in neuroncircuits, as well as in various other applications where bulkycapacitors are utilized as electrical current integrators. In addition,each of the synapses 104 may be implemented based on a memristorelement, wherein synaptic weight changes may relate to changes of thememristor resistance. With nanometer feature-sized memristors, the areaof neuron circuit and synapses may be substantially reduced, which maymake implementation of a very large-scale neural system hardwareimplementation practical.

Certain aspects of the present disclosure support methods for solvingthe problem of truly replaying a neural firing pattern that has beenlearned by one or more neurons (e.g., the neurons illustrated in FIG. 1)in the absence of original stimulus. Furthermore, the methods proposedin the present disclosure solve the problems of fast learning, learningrefinement, association, and memory transfer after the original stimulusis no longer present.

Current methods of learning a pattern with biologically inspired neuronmodels are functionally one-way methods: in order to determine whatpattern a neuron matches, one would need to try different patterns untilthe matching one is found. A method of true replay of what has beenlearned, either biologically or by machine is generally unknown. Thepresent disclosure provides a method to learn to replay a pattern(either forward, or reverse, or both) and to replay the true pattern bythe same neurons that produced the original pattern under stimulus and,furthermore, by excitation of the same neuron(s) that learned thepattern or that learned merely a subset of the pattern. Moreover,methods of the present disclosure allow for very fast learning and canbe highly scalable because multiple patterns, learned by one or moreneurons, can be replayed by the same neural structure (without addingneurons). Finally, learning of patterns and learning to replay can bothbe achieved in an unsupervised manner.

Learning replay may also provide a basis for learning a pattern betteror more completely. Moreover, it may alleviate the requirement to learnthe pattern during the stimulus. In addition to replay, the presentdisclosure provides a method for new or continued downstream or upstream(or both) processing operations without the original stimulus. Thus,using methods of the present disclosure, even if a stimulus is presentedonly briefly, the capability to replay an internal response pattern andcontinue to process the experience may become possible, thereby offeringsubstantial advantages for machine learning. The present disclosure notonly provides a method to replay true neural patterns, but also providemethods to refine learning of patterns without stimulus to improve orextend learning, as well as to transfer, consolidate or organizememories, to complete patterns or sequences of patterns, and toassociate or learn association of concepts and/or inputs without theneed to have a stimulus present. Finally, since these methods may beapplicable to concepts, sensory inputs, or signals at any level ofabstraction, context and content, the proposed methods may be bothfundamental and general.

Replay of Neural Firing Pattern

As aforementioned, prior methods of truly replaying a neural firingpattern are generally unknown. However, it may be required not only todetermine if a given pattern matches a stored (or learned) pattern, butto directly determine what the stored (or learned) pattern is. Inaddition, it may be required to reproduce the pattern in the originalafferents (inputs), via the neurons that originally produced thepattern. Further, it may be required to relate the pattern replay to theneuron that learned or matched the pattern (or had some correspondenceto the pattern). In addition, it may be required to reproduce a faithfulor high-fidelity replay of the stored (or learned) pattern, as well asto proceed with continued processing such as learning refinement,association, memory transfer using replay without the requirement forthe original stimulus to produce the pattern.

Although certain methods of pattern replay exist in the literature,these methods have several flaws. First, the pattern may not be playedby the same neurons that played the pattern under a stimulus. Therefore,this may not represent a true replay, but rather a copy of replay bysome other neurons. In this case, downstream effects from the afferentsmay be lost because the “replay” is not produced by the same afferents.

Second, elements of the pattern (e.g., spikes) may not be uniquelydistinguishable, and connection between a particular pattern element anda particular afferent may be lost. Therefore, this may not be a replayof the same pattern, but rather a replay of a different pattern (e.g.,an aggregated pattern). Third, there may be interference in the pattern(e.g., other activity in the afferents typically due to the “replay”trigger). Therefore, this may not actually be the same pattern, anddownstream effects, including neuron matching to the pattern, may bedisrupted.

Fourth, there may be interference (typically due to the “replay”trigger) in the output of neuron that learned the pattern. Hence,downstream effects from this neuron may be disrupted. Fifth, the“replay” capability may not be learnable in an unsupervised manner, orno method of learning at all may be known or available. Furthermore,other additional flaws may exist, such as only being able to “replay” inreverse, or only being able to “replay” with an approximate firing rate.These and other flaws will be discussed in reference to examples andanalogies.

Problem of Truly Replaying what Neuron Matched/Learned

FIG. 2 illustrates an example 200 of afferent neurons connected to apattern-matching neuron in accordance with certain aspects of thepresent disclosure. For example, a neuron 202 may match a pattern in aset of afferents (inputs) 204, whatever the cause or stimulus may be,whether external or internal. This situation is illustrated in FIG. 2where the neuron 202 may match a pattern produced by the output of theafferent neurons 204 (corresponding to their firing pattern 206, wherethe x-axis may be thought of as time or firing rate or other codingdimension). In this case, the term afferent is not meant to infer anyparticular meaning in terms of stage or layer of processing except thatit represents an input to the neuron 202 (i.e., afferents from the pointof view of neuron 202). The afferents 204 may be merely upstream neurons(e.g., upstream of the neuron 202), or sensory neurons, or neurons inany particular stage or layer of processing.

Typical neurons may be modeled as leaky-integrate-and-fire (LIF)neurons, or as dynamic spiking neurons, or even simple sum and sigmoidfunctions. Regardless, these operations may be one-way functions sinceit may not be possible operationally to determine what specific signalcombination or pattern the neuron 202 matches without trying differentpatterns until a match is found (i.e., without analyzing the internalssuch as synaptic weights and delays). There may be a fundamentaldifference between being able to store information and whether or not(and how) this information can be read out of memory. The question ishow a learned pattern can be read out (replayed).

Problem of Replay Fidelity

Ideally, the read-out pattern should be faithful enough to the originalto be recognized by a neuron that learned the original pattern during astimulus. The question is how the read-out pattern can be reproduced sowell that the very neuron that learned it can recognize it.

Problem of Offline Processing

The inability to truly replay a pattern without the original stimulusmay be a critical limitation for machine learning because it may requirethat an input or stimulus is present long enough for all aspects oflearning to occur. Yet, learning may need to occur at multiple stages ina downstream processing network (or even in upstream due to a feedback)or in a different order than presented in real-time. The question is howcontinued learning, refinement of learning, transfer of memory(learning), and other various downstream operations (or upstreamoperations or feedback) can be proceed once the original stimulus(input) is over.

Flawed “Replay” Attempts

First, one might consider just stimulating a neuron that matched apattern (e.g., the neuron 202 in FIG. 2) to fire (spike output) again,but this would have limited use because it may not provide a method forreproducing the same downstream effects at other neurons (e.g., neurons208 and 210 in FIG. 2). This would be somewhat like claiming merely bystating “that's the person A” is a replay of seeing the person A. Withthe former, one cannot examine features of the person A, compare them,or proceed with any further processing. Therefore, a method may berequired of replaying the original pattern (or substantially similarpattern) produced by the afferents via those same afferents (not otherneurons), and without interference to the pattern. Otherwise, thedownstream effect would be lost or incomplete. Therefore, if a stimulusis presented only briefly, the capability to replay an internal responsepattern and continue to process the experience would be a substantialadvantage for machine learning.

Second, it should be noted the importance of distinction as to whichneurons replay the pattern. For example, a network may only be able toplay the same pattern via different neurons (not the originalafferents). Even if those different neurons were also provided as inputsto the neuron 202 from FIG. 2, the question is how the neuron 202 wouldrecognize this as the “same” pattern without having to manuallyintervene to connect up these different inputs to have the same effectas the neuron 202 learned from the afferents. A clear demonstration ofthis flaw is that the neuron 202 will not recognize the copy played bythe different neurons. Clearly, the “replay” via different neurons doesnot represent a true replay at all, and it may be ineffective in termsof any downstream effects, including the neuron that learned thepattern.

Third, the relevance of pattern-matching neuron in replay is important.If the pattern matching neuron itself matches the original play as wellas the replay, a measure of fidelity may be established. Moreover, ifthat neuron is involved in invoking that replay, there may be abi-directional association of the pattern and the pattern-matchingneuron. There is thus motivation to relate the replay of the pattern tothe neuron or neurons that learned the pattern. In other words, one maylike to have the neuron 202 from FIG. 2 involved in the control orinducing process of replaying the pattern that the neuron 202 learned(or at least matched). For example, there could be a requirement toreplay a pattern learned by the neuron 202. Ideally, the replay triggerwould involve the neuron 202 and have the same downstream effectspossible, including recognition of the replayed pattern by the neuron202 itself. While it may not be necessary that the pattern matchingneuron directly induce the replay, it may be of interest that thisneuron is involved in the process, e.g., even if it results in a reversereplay first (or some other transform of the original pattern) and theneventually in a transform back to the original version via the originalafferents.

Fourth, playing an aggregate, derivative or sum of pattern elements(whether forward or reverse, compressed or not), firing rate or otherfunction of a pattern may not be a replay of that pattern. An analogywould be that typing ‘qwert[space]y’ on the keyboard can be “replayed”by typing the key ‘x’ on the keyboard five times followed by a ‘space’then ‘x’ again; yes, there are six letter key presses and the lastoccurs after a space, but which ‘x’ corresponds to which of the elementsof the pattern is unknown. The point is that one ‘x’ cannot bedistinguished from another ‘x’ in the so-called “replay”, and,therefore, it may not be possible to represent a specific relationbetween the unique letters in the original pattern. To summarize, whatis required is a replay of the actual original pattern via the sameafferents without interference.

Problem of Memory Transfer

Patterns have meaning only in terms of what neurons produced thepattern. For an analogy, if one were to state a sequence of numbers, thesequence has meaning in terms of not merely how many numbers are in thesequence and their relative order, but what the specific numbers are.The process of “transferring” memory by learning an output of a firstlayer with a second layer and then learning the output of the secondlayer with a third layer does not represent the memory of the firstlayer pattern being transferred. In order to transfer memory of thefirst layer, the third layer would also have to learn the first layeroutput. Without the original stimulus, this would require replay, not ofthe second layer, but of the first layer.

Generality of Problem

Biological evidence of replay of patterns has been observed in vivo, inparticular, in the hippocampus, visual system and other brain areas.Such replay may occur forward and reverse, compressed in time anduncompressed. However, the mechanism causing such replay is unknown.Further, recent evidence has shown that hippocampal cells may match orcorrespond to upstream patterns or states corresponding to “place” orepisodic aspects of behavior, but they may later be “erased” or reset,while memories may be retained. One possibility is that such memory isconsolidated into other or longer-term memory.

In general, the replay may be in use since it may apply to any sensorymodality or at any conceptual level in order to reverse the learning ormemory function. Thus, the replay may represent an important generalcortical problem to which a general solution would be very valuable.

Methods of Replay and Associated Methods of Further Processing IncludingLearning Refinement, Association, and Memory Transfer

Certain aspects of the present disclosure support methods of replay, andassociated methods of further processing including learning refinement,association, memory transfer and more.

Replay

A method of component replay proposed in the present disclosure maysolve the aforementioned problems of pattern replay. The componentreplay may be generally defined as the replay of pattern in afferentssubstantially referenced by a particular neuron via the same afferents.The particular neuron that references the pattern (reference neuron) mayrespond selectively in relation to the pattern and learn thatselectivity. Aggregate patterns or larger or longer scale patterns maybe replayed by combining component replays. Component replays may thusbe utilized to generate systemic replays in neural networks.

The general method of component replay illustrated by a flow diagram 300in FIG. 3 may comprise learning (or at least matching) a pattern in aplurality of afferent neuron responses using one or more patternlearning (or at least matching) neurons. Also, the method of componentreplay may further comprise learning one or more relational aspects ofthe learning neurons and the afferent pattern using one or morerelational learning (or at least matching) neurons.

As illustrated in FIG. 3, the general method of replay may compriselearning a pattern and hence reference neurons may be referred to aspattern learning neurons. However, learning of the pattern by a patternlearning neuron may not be required. Rather, the pattern learning neuronmay be replaced by a reference neuron that may or may not learn, may ormay not match a pattern in afferents, and may not even be connected tothe afferents. All that is required may be a reference that correspondsto the pattern in the afferents (i.e., consistent in having somerelation to the pattern, such as firing at a particular time relative toa particular afferent pattern). It should be noted that the mid-layer ofcortical fabric can be generally referred to as either reference neuronsor pattern learning (or at least matching) neurons.

The neural component replay concept will be described in detail in atemporal coding context, although the method may be general and may beapplied to firing rate coding, coincidence coding, or other signalcoding variations. Delays due to signal propagation along axons, passingthrough relays, or dendritic processes or even at synapses may beabstracted.

A Demonstrative Replay Embodiment

FIG. 4 illustrates an example neural connectivity diagram 400 of a firstdemonstrative embodiment of the present disclosure in such a temporalcoding context. Neurons 402 and 404 may be representative of afferents,a neuron 406 may be a pattern-matching or learning neuron, and neurons408 and 410 may be relational-aspect learning/matching neurons. In thistemporal coding context, delays can be represented on connections byrectangles of various widths. The larger the rectangle is horizontally,the longer the delay may be. It should be also noted in the diagram 400that connections only occur where arrows terminate (i.e., not merelybecause a line crosses another). The same convention will be used inother neural connectivity diagrams.

Under a stimulus 412, afferent neurons 402 and 404 may fire in atemporal pattern 414 depicted in a diagram 416, where the x-axisrepresents time and vertical bars indicate the time of firing of eachneuron. The stimulus 412 may come from any of a number of sensorymodalities such as touch, hearing, smell, taste, vision, etc, or merelyfrom upstream in a neural network. It should be noted that if the neuron406 is a leaky-integrate-and-fire or other typical neuron model andthere is a particular threshold of two inputs for firing, then theneuron 406 might fire when the neuron 402 fires somewhat before theneuron 404. This may be because the connection from neuron 402 may needto pass through a larger delay before reaching the soma of neuron 406,while the connection from neuron 404 may pass through a smaller delay.Thus, the neuron 406 may match that particular temporal sequence, asexhibited in the diagram 416, and it may fire a spike 418. In thediagram 416, the neuron 406 may fire immediately after the delays, butthis is merely an example and there may be some delay due to processingby the neuron 406 (e.g., a time constant).

It can be observed from the diagram 400 in FIG. 4 that the neuron 408may receive input from the afferent neuron 402 and thepattern-matching/learning neuron 406. The delays that the input fromneuron 402 encounters may align the signal to the response by the neuron406. In effect, the neuron 408 may match the relation in timing betweenthe afferent neuron 402 and the response of pattern-matching/learningneuron 406. The neuron 410 may achieve a similar effect with theafferent 404. It can be observed from the diagram 416 in FIG. 4 that,under the original stimulus 412, these relational-aspect learningneurons 408, 410 may fire in response to the respective relationalaspects (e.g., a firing 420 in the diagram 416). In the diagram 416, thedelay between the firing 418 and the firing 420 may be small, butnon-zero. This is also merely an example; the actual delay may be zeroor greater. Moreover, there may be a delay inserted between the input of406 on the dendrites of neurons 408, 410 and the somas of neurons 408,410.

In order to replay the pattern in this first embodiment, a control 422may be applied for the original afferents to spike. This control may befrom an input other than the stimulus 412 that produces the originalpattern. It should be noted that, in this case, the control may besynchronous, as illustrated in a diagram 424 in FIG. 4. Because of thedelays that neurons 408, 410 have learned (or at least match), theneurons 408, 410 may fire at delayed times, rather than at the same time(spikes 426 and 428 in the diagram 424). It should be noted that theinputs from neurons 402, 404 may need to be strong enough to cause theneurons 408, 410 to fire (not necessarily immediately, but at leasteventually or with some background help such as oscillation excitationor reduced inhibition or additional coincident input from othersynapses, as denoted by a stimulus 430). Since the neurons 408, 410 maybe fed back to the neurons 402, 404, they may cause those afferents tofire in the reverse of the original pattern, thereby reverse replayingmay be achieved, as illustrated by a reverse temporal pattern 432 in thediagram 424. It should be noted that an inhibitory control 434 may beused to prevent the feedback from causing the afferents 402, 404 to firewhile the original stimulus 412 is present, although variousalternatives such as intermediaries may be substituted. Moreover, thefeedback may be allowed to impact the original afferents even during thestimulus.

The advantages of this first demonstrative embodiment can be that thereplayed pattern may be true and exact, and may be produced via theoriginal afferents. However, this embodiment can also have certaindisadvantages. In an aspect, the pattern may be reversed and the controlmay be on afferents (the neurons 402, 404 may fire synchronously beforerecreating in the reverse pattern), and thus may impact downstream(including behavior of the neuron 406). In addition, the neuron 406 maynot match the reverse pattern. Moreover, in this embodiment, scalabilitymay be limited because a relational-aspect learning neuron may berequired per pattern per afferent. Furthermore, the replay may alsorequire synchrony of the control.

Forward and Reverse Replay

A second replay embodiment illustrated in FIG. 5 may solve the problemof replaying a non-reversed pattern by double reversal. Effectively, theaforementioned method associated with FIG. 4 may be repeated, so that asecond layer 2 and layer 3 (referred to as layer 4 and 5 in FIG. 5) maybe added to match the reverse pattern, the relational aspects of thereversed pattern, and either the original afferents (illustrated in aneural connectivity diagram 502) or the relational-aspect learningneurons p and q (illustrated in a neural connectivity diagram 504).Thus, the proposed method may replay either the reverse pattern or theforward pattern (by controlling either the pattern-matcher x or thereverse-pattern-matcher x′, illustrated in FIG. 5). The advantage of thediagram 504 (using the relational-aspect learning neurons for relationlearning at the reverse level) may be that reversal learning can beperformed on-line (i.e., at the same time as pattern learning). With theneural connectivity diagram 502, reversal learning may be performed byinducing the reverse pattern.

It should be noted that a major difference between methods 502 and 504may be the use of relational-aspect learning neurons instead of theafferents for the second “replay” stack (layers 4 and 5). In the diagram504, on-line learning of the second-reversal can be done because of thisdifference. However, the neuron x′ may not be exposed to the afferentsbut rather to a transform of the afferents.

The advantages of the second embodiment variations illustrated by thediagrams 502, 504 in FIG. 5 can be that the forward and/or reversepatterns may be replayed. However, there may still be a scalinglimitation. In an aspect, one relational-aspect learning neuron may berequired per pattern per afferent, and the control on afferents mayaffect downstream. The preferred embodiment discussed later in thepresent disclosure may overcome these disadvantages.

Memory Transfer

Transfer of learning from one neuron, neural array or cortical area toanother may be a critical component for machine intelligence. The replaymay serve a key role in this process. To explain this, the neuralconnectivity diagram 502 from FIG. 5 can be considered. For introducingan aspect of memory transfer, it can be ignored the fact that the replayin the circuitry of FIG. 5 is a reverse of original pattern. A detaileddescription of memory transfer and further processing aspects will begiven further below along with circuits that replay a forward version ofthe pattern. Meanwhile, the following example may serve to introducefurther processing.

The transfer of learning may work as follows. While the stimulus ispresent, the first circuit (represented by layers 2-3 in the diagram502) may learn the pattern. Then, off-line (without the stimulus), thememory of this learning may be transferred to the second neuron circuit(represented by layers 4-5 in the diagram 502) by inducing the patternwith the first circuit (via layers 2-3) and allowing the second neuroncircuit (layers 4 and 5) to learn (receive) the pattern. Moreover, oneor more additional neurons (not shown in the diagram 502) may learn thepattern during replay (whatever circuit they belong to). It should benoted that, after the off-line step, the original circuit (layer 2 and3) may even be erased and reused for other purposes while stillretaining the memory of the learned pattern in the second neuronalcircuit (layers 4 and 5).

It should be noted that, with this embodiment, the transferred memorymay be with respect to the same original afferents, as being desirable.There may be no reliance on the first circuit (which might even beerased or may “forget” the pattern). In an aspect, another neuron, e.g.,a neuron y (not shown in the diagram 502, but in any layer, may serve toreplace the neuron x) may be trained (learn) the pattern during replay.Then, if the original stimulus is present again, the neuron y mayrecognize it (even if layer 2 and 3 are erased), so the pattern may bematched and replayed whenever desired. This memory transfer capabilitymay be generally available with proposed methods and not only with thisembodiment, although the transfer may not necessarily involve reversalat each stage depending on the embodiment.

Furthermore, the memory transfer may be only one extension of replay. Inan aspect, the replay may also be used to refine learning. By replayingthe pattern, downstream neurons (e.g., the neurons 208 and 210 in FIG.2) may be re-exposed to a pattern via the same afferents so thatlearning may continue via synaptic plasticity or other means. Further,the replay may even be used to refine the learning by the neuron thatlearned the pattern, i.e., the neuron x in the diagram 502 in FIG. 5 orthe neuron 202 in FIG. 2. In an aspect, learning of a pattern may takeone or more stages (learning during a stimulus and one or morerefinement stages by replay without the stimulus). An earlier stage ofpattern learning (e.g., during the stimulus) may match the pattern bymatching a subset of the elements of the pattern. A later stage ofpattern learning (e.g., during replay) may pick up additional elementsof the pattern to improve matching. A key insight in this is that therelational-aspect learning neurons may learn relational aspects betweenthe pattern-matching neuron's behavior (firing) and elements of theafferent pattern that are not yet relied upon by that pattern-matchingneuron to match the pattern.

Further, once neurons learn particular patterns, a downstream networkmay compare patterns by replaying those patterns (or compare replayedand original patterns) and learn differences or similarities such asclassification of patterns into a group. By replaying patterns belongingto one or more groups, higher-level neurons may learn the generalaspects of patterns belonging to particular groups (e.g., similarfeatures).

Introduction to Preferred Embodiment for Pattern Replay

The preferred embodiment for pattern replay may comprise a scalablemultiplex tapestry or cortical fabric in the sense that it can be usedin a general way (i.e., regardless of context or content or coding,layer or level, or stage of processing) and scaled according to a desireor requirement. This particular embodiment may have all the advantagesand none of the disadvantages described above. The exact or similarpattern may be replayed in forward or reverse via original afferents.Moreover, it may be compact and scalable because each relational-aspectlearning neuron can deal with multiple patterns. Moreover, control maybe induced via the neuron that learned the pattern and no controlsynchrony may be required. The afferent pattern may be clean. Patternlearning may be fully online, if desired (or offline, if desired). Aneural connectivity diagram 600 in FIG. 6 outlines a basicimplementation of the idea for forward replay (reversal may be achievedby extending the diagram 600 according to the above methods).

It should be noted that relational-aspect learning (or at leastmatching) neurons 602 and 604 in FIG. 6 may have inputs from two patternlearning (or referencing) neurons 606 and 608. In general, this could beany number of pattern-learning neurons. This may allow for thescalability mentioned above. Furthermore, the control may be achieved byexciting the pattern matching neuron (i.e., the neuron 606 and/or theneuron 608) in order to get replay of the patterns they matched(respectively). When the neuron 606 is excited, replay of the patternlearned by the neuron 606 may be achieved. When the neuron 608 isexcited, replay of the pattern learned by the neuron 608 may beachieved. In the example result with leaky-integrate-and-fire neuronsshown by graphs 702, 704 in FIG. 7, the truth of the pattern replay canbe evident from the fact that the neurons 606 and 608 may re-fire uponthe respective replay (matching the replay of the patterns theylearned/match). One may choose to suppress such re-firing as one meansto prevent a looping replay.

In the preferred embodiment, the relational-aspect learning/matching maybe achieved by learning/matching the difference in delay between adelayed pattern-matching neuron input and afferent signal timing.Although the delay may be illustrated as if occurring on the dendrite ofneuron(s) 602, 604, that delay may be axonal or dendritic, or from someother source.

The preferred embodiment will be described further below. From thispoint onward, the discussion is in reference to this preferredembodiment unless otherwise stated.

FIG. 8 illustrates example operations 800 for neural component replay inaccordance with certain aspects of the present disclosure. At 802, apattern in a plurality of afferent neuron outputs may be referenced withone or more referencing neurons. At 804, one or more relational aspectsbetween the pattern in the plurality of afferent neuron outputs and anoutput of the one or more referencing neurons may be matched with one ormore relational aspect neurons. At 806, one or more of the plurality ofafferent neurons may be induced to output a substantially similarpattern as the referenced pattern by the one or more referencingneurons.

Learning Replay

Having described how true replay can be achieved (given configureddelays, connections, and controls), a method of how to learn replay andcontrol learning within a network (automatically or unsupervised) willalso be described. Unsupervised learning will be described in detail,although supervised learning could also be used. In order to describelearning replay in detail, a description will be given in the context ofthe preferred scalable embodiment (again with temporal coding) withdynamic spiking neurons and plasticity.

A neural connectivity diagram 900 in FIG. 9 illustrates a model of thepreferred embodiment, showing only a few representatives of each neuronrole although this tapestry may be created at various scales eitherlarger or smaller and with different layers (roles) having differentpopulation distribution/number or ratio of neurons and some roles can becombined into one neuron. In FIG. 9, the afferents may be represented byneurons a through d, pattern learning neurons by w through z, andrelational-aspect learning neurons p through s. While these neurons mayhave the same or different internal models, the connections may beimportant. To aid explanation of such connections, the outputs ofpattern-learning neurons can be referred to as efferents (inputs todownstream processing or feeding back to upstream processing). Sinceafferents may actually be sensory neurons (vision, touch, hearing, etc)or a preceding layer of neurons or neurons at any stage or conceptuallevel in a neural network, a stimulus can be referred to as whatevercauses neurons a through d to fire in one or more patterns. Whateverinput or trigger causes particular neurons x through z to fire can bereferred to as controls. These signals (afferents, efferents, controland stimulus) typically would connect outside the neural tapestry 900,but this may not be required and other internal signals may also extendfrom or to points beyond the neural tapestry 800. For clarity, theneural connectivity diagram 900 may focus on mainly relevant connectionsand aspects.

It should be noted that the general method remains as for the preferredembodiment described above. However, further details are now describedwhich relate to learning. First, the connectivity will be described withreference to example structural aspects that may relate to suchconnectivity.

Learning and Structure

As described earlier, the pattern learning neurons may learn a patternin a plurality of afferents (but not necessarily all of them). Suchconnectivity may be described structurally as cruciate in a biologicalsense (the dendrites might spread out intersecting axons of theafferents), sampling the afferents with different potential delays.Also, they may connect to one another to laterally inhibit each other(spines 902 in FIG. 9) so that they may compete to learn differentpatterns (this may be direct, as shown, or via inter-neurons or otherinhibitory or competitive mechanisms). This inhibition may be general(i.e., post-synaptic). Also, as described earlier, the relational-aspectlearning neurons may receive inputs from the pattern-learning neuronsand inputs from the afferents (generally one-to-one). Structurally, theformer may be described as basal dendritic processes parallel to thelearning neurons' axons (not necessarily just one) and the latter may bedescribed as apical dendrite processes that connect to (preferably) onlyone afferent's axon.

Such biological structural design may not be necessary or critical, butit is meant to point out how connectivity may be represented in physicalspace and constraints that may be represented physically. Theimplications of this will be explained further below. Therelational-aspect learning neurons' axons may also connect back(feedback) to the afferent neurons they are paired with (again generallyone-to-one). This might be represented as an axonal process extendingvertically down to the afferents. Finally, the output of the afferentsmay inhibit excitation of those afferents by the relational-aspectlearning neurons via inter-neurons (one inter-neuron per afferent ormany-to-one, one-to-many, or many-to-many). Again, the use ofinter-neurons can be an example. However, in this case, these inhibitoryconnections may be specific to the particular excitatory connectionsfrom the relational-aspect learning neurons (i.e., pre-synapticinhibition). Another option may be to use an inhibitory connection thatis general (i.e., post-synaptic inhibition). Variation may be possible,including aggregating the inhibition effects into one inter-neuron orusing intermediary relays.

Synaptic Plasticity and Structural Plasticity

Synaptic weights may be learned using methods such asspike-timing-dependent plasticity (STDP) or other rules such as Hebbianrules (e.g., Oja or Bienenstock-Copper-Munro (BCM) rules). It should benoted that the described mechanisms can be general enough to learn oneor more spikes of the afferent or learning neuron as long as the numberof synapses/delays are not restrictively constrained in this respect.The preferred mechanisms comprises a modified STDP learning rule, butvariations can also be used such as incorporating consideration of inputfrequency, or pre/post spike order. The learning relations may be variedacross neuron or layer, as illustrated by a neural connectivity diagram1000 in FIG. 10, which may represent a portion of the diagram 900 fromFIG. 9.

Learning may be constrained by particular aspects of the aboveconnectivity and structures. This may be achieved by structuralplasticity or constraints on delays, weights and or inputs. In thepreferred embodiment, structural plasticity may be applied to move orgrow/delete spines (and thus synapses). From a computational standpoint,this may be modeled by reuse of unused synaptic resources. Effectively,when a synapse weight decays below a threshold, the synapse may bereused by reassigning the synapse with one or more of a new weight(preferably at or slightly above the threshold), new delay or input(neuron). In the preferred embodiment, only a new weight and delay maybe provided, and one option can be to limit the change in delay (e.g.,by amount and/or direction as if structurally growth-rate constrained).

Furthermore, the structural plasticity may be constrained or bounded.For example, distal dendrite synapses may be constrained to a longerdelay while apical dendrite synapses may have short or wide-varyingdelay. If dendritic processes run parallel to axons, then this may allowsampling at various delays and thus learning of particular correspondingdelay. In contrast, if an input neuron is far from the receiver (e.g.,afferent input to relational aspect learning neuron), the delay may beconstrained to be a relatively high value. In addition, equidistantstructural components may be assumed to have the same or similar delay.It should be understood that these are merely examples in keeping withthe concept of the present disclosure. In the preferred embodiment, thedelay encountered by afferent inputs to the relational-aspect learningneurons may be fixed at a relatively large value coinciding with theirstructural distance, while synapses may have a variable delay within alarge range for inputs from the learning neurons. Moreover, the fixeddelays for the former may be set to the same or a similar value acrossrelational-aspect learning neurons (as if the distance to the pairedafferent is similar regardless of the pair). It should be also notedthat one-to-one or other relations may be structurally represented bycolumnar-type organization and routing of axonal or dendritic processesvertically or horizontally, as the case may be.

It should be noted that, in the neural connectivity diagram 1100 in FIG.11, delays encountered by inputs to the relational neurons from theafferents may be coincident. This may be due to structural constraints(e.g., distance between the layers) or in development. For example,during development, the pattern neuron firing may be inhibited whileafferents may fire synchronously. Thus, the relational aspect neuronsmay learn coincident delays for the afferents. This may occur, forexample, using STDP or other learning rule. Then, after development,those coincident delays may be learned with a large weight and may notchange during normal stimulation. As a result, the relational aspectneurons may fire with delays depending on the relative delays in theafferents due to the stimulus. The learning and replay processes maythen proceed as explained above.

Alternatives for Relational-Aspect Learning

One way to achieve relational-aspect learning computationally may be tohave many synapses (or potential synapse sites/spines) with differentdelay connecting (or potentially connecting) to the same input axon anduse only synaptic plasticity to adjust weights, as illustrated in agraph 1202 in FIG. 12. Another way may be to have structural plasticityand only one or a few synapses that, if they decay, may be reassignedwith different delay to the same input (e.g., slide along thedendrite/axon parallel processes), as illustrated in a graph 1204 inFIG. 12. Both methods may represent the range of potential connectionsdepicted in an example structure 1206 in FIG. 12. The structure 1206 maycomprise parallel processes of dendrite of a neuron 1208 and axon of aneuron 1210. The diagrams 1202 and 1204 show all synaptic states in thenetwork by input (x-axis), delay (y-axis), and weight (z-axis). Thepositioning of points (synaptic states) belonging to therelational-aspect learning neurons on the plots 1202, 1204 illustratethat there can be many potential synapses with the first case and fewwith the second case. However, in both cases, one or more synapticweights may be reinforced at the correct delay and for the correctinput.

Both methods have been found to work well although the first one maylearn faster since the spines all exist simultaneously rather than being“tried out” in some sequence. However, while the first method may befaster, it may also be subject to imbalance of inputs from differentpattern-matching/learning neurons. The second method may be moreresource efficient since, in the first method, many of the delay taps(synapses) may end up being unused (see the graph 1202 in FIG. 12). In asense, these methods are merely different points on a spectrum of thespeed or dynamics of spine/synapse motility and structural plasticity.

Further, the first method may have a higher tendency to learn more broadcausal input correspondence unless homeostasis is used to counteractthis. This may be due to the classical STDP having a long long-termpotentiation (LTP) tail (positive footprint), as illustrated by a plot1302 of a graph 1304 in FIG. 13. The x-axis represents thetime-difference between pre- and post-synaptic spikes, where thepositive x values correspond to a causal case (pre-synaptic spike isbefore post-synaptic spike).

The above problem may be avoided by reducing the positive STDP footprintto a shorter time interval, as illustrated by a plot 1306 of a graph1308 in FIG. 13. This may be performed by changing the plasticity curvedirectly (various causal or non-causal variations are possible, such aszero-mean or offset Gaussian), or by using homeostasis to regulateneurons based on their overall activity (e.g., a firing rate) over alonger time frame. The homeostasis may be utilized by increasing ordecreasing a general multiplicative modulation of aggregate input to thesoma based on whether the long-term firing rate of the neuron is aboveor below a range (i.e., an upper and lower target value, respectively)in a step-wise or gradual manner. In an aspect, an effect of this may bethat even though STDP may otherwise cause weights to increase (ordecrease), the homeostasis effect may reduce (or increase) the effect ofthe synaptic inputs.

When viewed in combination, this effect may be represented as in thegraph 1308 where the zero-level is shifted up so that what might be anincrease by STDP can actually be a decrease after homeostasis effects(see the plot 1306). The weights that, over the long term, actuallyincrease may be limited to a narrower or expanded to a wider region ofthe horizontal axis (time difference between pre- and post-synapticspikes).

A hybrid approach may utilize both homeostasis and alter the curve, asillustrated by a plot 1404 of a graph 1402 in FIG. 14. One reason forpushing the tail down further (away from the zero-weight adjustmentlevel) can be to narrow the temporal coincidence resolution (which maybe particularly useful for the relational aspect learning neurons)because homeostasis may not bring down weights that were raised earlierin the learning process (the plot 1404 is being near the zero line1406). It should be noted that a weight adjustment near zero might notchange a weight substantially. Alternatively, weight decay (slow decayof weights toward zero) may be used in combination with the learningprocess.

Counter-Intuitive Behavior

At a first glance, it may be required that pattern-learning neurons donot considerably alter relational-aspect neuron firing times duringlearning. Since the relational-aspect neurons receive inputs from bothafferents and pattern-learning neurons, a balance of inputs might be aconsideration. In fact, the pattern-learning neurons may be unlikely tosignificantly alter the relational-aspect neuron timing unless weightsfor those connections are considerably stronger than those for afferentsor multiple pattern-learning neurons fire substantially at the same time(an effect counterbalanced by lateral inhibition). In any case, this maybe avoided or substantially inhibited via spine or synapse constraints(weights, delay). A combination of structural plasticity (spine locationon delay-variant dendrite) and non-classical STDP (e.g., resourceconsiderations) can also render the above unlikely.

However, even if imbalance exists and upper layer spike timing isaltered, it may not be necessarily something to be avoided. Asillustrated by a neural connection diagram 1500 in FIG. 15, a feedback1502 of relational-aspect firing (which may require being significant inorder to enable replay) back to afferents may also alter afferenttiming. In other words, there may be a feedback loop so that timingalterations in upper layers cause timing alteration in lower layers.But, what is important is reproducing the afferent pattern, notreproducing the stimulus. Afferents may be thought of as atransformation of the stimulus. If the transform is different, it doesnot necessarily matter as long as that learning is based on the sametransform. In other words, during learning, the afferent pattern maychange and this changed afferent pattern may represent the patternlearned, and that may be replayed.

FIG. 16 illustrates example operations 1600 for neural component replayin relation to structural plasticity and structural constraint modelingin accordance with certain aspects of the present disclosure. At 1602, apattern in a plurality of afferent neuron outputs may be referenced withone or more referencing neurons. At 1604, one or more relational aspectsbetween the pattern in the plurality of afferent neuron outputs and anoutput of the one or more referencing neurons with one or morerelational aspect neurons may be learned using structural plasticity. At1606, one or more of the plurality of afferent neurons may be induced tooutput a substantially similar pattern as the referenced pattern by theone or more referencing neurons.

Controlling Replay

In order to replay a learned pattern, a control may be used to excitethe neuron that learned the pattern, e.g., neurons w, x, y or z in anetwork diagram 1700 from FIG. 17. Such control may merely be anothersynaptic input to the pattern-matching/learning neuron, as illustratedin FIG. 17. Depending on the input balance, such synapses may havestronger weights or the input may be driven during an excitatoryoscillation, which may effectively increase the neuron's input ordecrease the firing threshold (or during a lull in inhibitoryoscillation). While oscillation may be unnecessary, it may be analternative. The example 1800 from FIG. 18 illustrates a sinusoidalexcitatory oscillation 1802. It should be noted that the peak of theoscillation can be when a learning neuron 1804 is controlled to spikeand induce the afferent pattern via the relational-aspect learningneurons.

The oscillation 1802 may be applied to the membrane (at soma with low orno delay) of neuron 1804 or at the synapses (thus incurring a delayaccording to the synapse/connection). The oscillation 1802 may begenerally applied to all the neurons in a neighborhood, so theoscillation in the example illustrated in FIG. 19 may boost the effectof the learning neurons on the relational-aspect learning neurons aswell. It should be noted that the replay might repeat except that theoscillation used in this example has a trough that suppressesre-excitation after the pattern.

Another way to control replay can be with bursting. The examples fromFIG. 19 and FIG. 20 illustrate how bursts by the neuron x or y may beused to cause the relational-aspect learning neurons to fire. This isjust an example, since bursting could be used at any layer/neuron.

However, both bursting and oscillation may be unnecessary and may havedisadvantages, and thus may not be preferred. Nevertheless, if burstingis a desired feature, it may be recommended that bursting can be used(a) internally to the circuit (i.e., not at points that may have inputor output connections for the local circuit) and on less-plasticconnections (or non-plastic connections). First, if bursting is onlyinternal to a circuit, it may obviate interference in connected circuits(if certain connections in the array are local-only). Second, ifbursting is on less-plastic or non-plastic connections in the circuit,it may obviate some interference with learning aspects internal to thecircuit.

A variation of the replay architecture 1700 from FIG. 17 can beconsidered where afferents and reference neurons (e.g., the neurons aand x, respectively) may not burst (just operate as normal) butrelational neurons (e.g., the neuron p) may burst (optionally only inreplay mode). This may avoid some of the bursting disadvantages forafferents and reference neurons because, by definition, they may notburst. Moreover, one disadvantage of bursting may be cancelled out inrelational neurons: the relational neurons may not have to burst duringplay; this lower level of activity may then be configured such that itmay be insufficient to significantly influence the afferents (feedbackloop). Then, removing the inhibitory feedback connections that blockrepetition may be considered (if repetition is desired or not). Also,several subtle but important issues can be avoided due to a combinationof delays, learning and bursting in this case because relational-aspectlearners (the only bursting neurons) may be one-to-one connected withafferents and may be causal in that mode. However, there may still be adelay-burst-learn combination problem, if afferents also run learningwhile replaying. To avoid this, relational-neuron inputs toafferent-neuron connections may be pre-wired (e.g., in development).

A neural connectivity diagram 2100 in FIG. 21 exhibits the aboveprinciples with a single neuron 2102 representing afferents, a neuron2104 representing reference neurons and a neuron 2106 representingrelational-aspect-learning neurons. It should be noted that afferentsmay receive input from upstream or stimulus, and their output may beused downstream or by other reference neurons (pattern matching neurons)such as for matching other patterns, memory transfer, comparison, memorytransfer, and so on. The reference neurons may receive control inputfrom upstream or other afferents and their output may be used downstreamfor generalization, specialization or further processing. However, therelational aspect learning neurons may have only connections internal tothe circuit. Thus, according to the description above, only those mayburst in one embodiment of the present disclosure.

Also, bursting may be allowed only during replay (or other mode) byaltering the dynamics of a neuron in response to a control input. Forexample, while data input may alter the state of a neuron (current orvoltage), a control input may alter effective conductance, resting orthreshold potentials, and other dynamics parameters.

If a greater control is generally desired, the preferred embodiment mayrather be based on expansion. Another way to control replay can be byexpanding the network, as illustrated in FIG. 22, in other words, bygeneralizing from one-to-one relations to one-to-many or many-to-onerelations or even many-to-many relations. It should be noted thatalthough it was illustrated in the diagram 2100 from FIG. 21 that theafferents and relational-aspect neurons were connected on a one-to-onebasis, this may not be required. This may present at least two possibleadvantages. First, multiple-to-one and vice-versa connections mayimprove robustness in the presence of noise or probabilistic firing.Second, if one input is insufficient to cause a receiving neuron to fire(a constraint imposed), then multiple inputs may overcome thislimitation.

FIG. 23 illustrates example operations 2300 for neural component replayin accordance with certain aspects of the present disclosure. At 2302, apattern in a plurality of afferent neuron outputs may be referenced withone or more referencing neurons. At 2304, one or more relational aspectsbetween the pattern in the plurality of afferent neuron outputs and anoutput of the one or more referencing neurons may be matched with one ormore relational aspect neurons. At 2306, one or more of the plurality ofafferent neurons may be induced to output a substantially similarpattern as the referenced pattern by the one or more referencing neuronsby bursting the output by the one or more relational aspect neurons.

FIG. 24 illustrates example operations 2400 for neural component replayin accordance with certain aspects of the present disclosure. At 2402, apattern in a plurality of afferent neuron outputs may be referenced withone or more referencing neurons. At 2404, one or more relational aspectsbetween the pattern in the plurality of afferent neuron outputs and anoutput of the one or more referencing neurons may be matched with one ormore relational aspect neurons. At 2406, one or more of the plurality ofafferent neurons may be induced to output a substantially similarpattern as the referenced pattern by the one or more referencingneurons, wherein signaling between at least one of the afferent neurons,the one or more referencing neurons, or the one or more relationalaspect neurons may comprise at least one of a rapid spike sequence orindependent spikes.

Component Replay and Systemic Replay

Replay of multiple (e.g., five) patterns after a short training durationusing the above described temporal coding model with synaptic andstructural plasticity is illustrated in FIG. 25. The replays areillustrated in the afferent section (L1 A) of boxes (absent the stimulus(S)) (relational-aspect learning neuron responses not shown in FIG. 25for purposes of clarity). It should be noted that in the case of the2^(nd) pattern, two neurons learned the pattern. Re-exciting the secondone may replay a more complete pattern (more afferents) (see pattern in“L1 A” under “II” second box). Thus, it should be noted that the methodmay replay a pattern more completely by exciting one or more of theneurons that learned the pattern. In addition, FIG. 21 illustrates howthe same architecture can multiplex (learn and replay multiple patternsI through V) with the same neurons. In other words, the architecture maybe very scalable.

Learning Refinement and Association

It should be noted that component replay may replay more than thepattern learned by a neuron. This point may appear subtle but it may bevery useful. A key insight can be that to refine learning, a methodwould require to replay a pattern better than it has been learned bywhatever entity will undergo refining in learning (or providing amissing piece). However, this may not mean that the pattern is betterthan any entity has learned the pattern.

In an example 2602 in FIG. 26, a flawed concept of refinement is toreplay only the portion of a pattern learned by the entity that will besubject to learning refinement. The question is how further refinementcan occur. In an example 2604 in FIG. 26, the replay may be of a morefaithful pattern reproduction than it is already learned by the entitythat will undergo learning refinement. However, this may not mean thatthe entity to undergo learning refinement is the only entity that haslearned anything about the pattern.

The fact that a particular neuron x learns to fire upon a particularafferent input may be actually due to the neuron x learning fewer thanall of the firings in the afferent input pattern. An example isillustrated in FIG. 27 where afferent d may not be relied upon for thepattern recognition by neuron x. As an analogy, it can be consideredrecognizing someone's face merely because of the eyes and nose. But, therelational-aspect learning neurons may learn the relation between thematching neuron's firing and potentially any one or even all of theelements in the afferent pattern. In other words, the relational-aspectlearning neurons may not be constrained by what elements of the patternneuron x is depending on. As an analogy, it can be considered a thirdperson who hears a first person saying “that's the person A” about asecond person. The third person may also see the second person (personA). Maybe, the first person recognizes person A only because of eyes andnose (suppose the rest of face of the person A is occluded). This maynot prevent the third person (who can see entire face of the person A)from learning the correspondence between hair of person A and the secondperson's identification of him as “person A”. Thus, relational-aspectlearning neurons may reproduce a different (e.g., larger, more complete,or different parts) of an afferent pattern than a learning neuron (orneurons) depend on for matching.

It should be noted, however, that the “afferent” may not need to beconnected to the pattern learning neuron (e.g., the neuron n in theexample 2702 in FIG. 27). As long as there is a paired relational-aspectneuron (e.g., the neuron t), the firing of neuron n may be associatedwith the afferent pattern and replayed with the afferents. This mayprovide an online method of associating other signals or activity with apattern being played by afferents that is being learned. While there maybe no need to learn the correspondence at the level of learning neurons,the correspondence may be retained for replay (and learned later or at ahigher level or by another neuron) where another piece of the pattern(such as the neuron d in FIG. 27) or a correlated neuron firing (such asthe neuron n in FIG. 27). Thus, as illustrated in FIG. 28, the proposedmethod may refine learning, compare, or proceed with other processingonce a stimulus is no longer present.

FIG. 29 illustrates example operations 2900 for neural learningrefinement in accordance with certain aspects of the present disclosure.At 2902, a subset of a pattern in a set of inputs may be learned with astimulus. At 2904, a relational aspect between elements of the patternand the subset of the pattern may be learned. At 2906, the pattern inthe set of inputs may be replayed using the learned relational aspectwithout the stimulus. At 4208, learning of the pattern in the set ofinputs may be refined without the stimulus.

Memory Transfer and Association

Given a method of replay, a method of memory transfer can be described.FIG. 30 points out a key distinction for meaningful memory transfer:without replay via original afferents, “transfer” of memory cannotactually occur because whatever the transferee learns, may not be anaspect of the original afferents. In an example 3002 of FIG. 30, thetransferee may learn the output of a pattern-matching neuron. The keyinsight can be that for useful transfer, the transferee may not need torely on the transferor afterward to recognize the pattern. In an example3004 of FIG. 30, this may be the case. Thus, replay via the originalafferents may be required for off-line (without stimulus) transfer ofthe memory. This replay may be component replay or systemic replayresulting from component replay elsewhere in a neural network.Regardless, the pattern recognizing transferor may be involved intriggering the replay, but the transferee may not rely on that.

To transfer a memory, the replay method may be used to replay thepattern via original afferents. Although this may seemcounter-intuitive, it should be noted that the transferee (the newpattern matcher) and associated relational-aspect neurons may beunconnected to the transferor (the old pattern matcher) and to anyassociated relational-aspect neurons (i.e., if the transferorhas/requires replay capability; otherwise the relational-aspect neuronsare not required). The transferee may learn the replayed pattern. Itshould be noted that the transferee may learn different elements oraspects of the pattern learned by the transferor. It should be alsonoted, analogous to the above description of association in relation tolearning refinement, that the transferee may learn a combination of theafferent pattern and a pattern in one or more additional input neurons(the neuron n in the example 2702 in FIG. 27). Except, here it may beeither due to replay of that other pattern as part of the replay or asmere coinciding “play” of those additional signals in order to associatethem with the afferent pattern. In either case, the one or moreadditional input may or may not be input to the transferor, even inputto the transferee or relational-aspect learning neurons.

It should be noted that once a memory is transferred, both thetransferor (pattern-matching neuron(s)) and the relational-aspectlearning neurons may be reused/erased/reassigned or undergo new learningwithout disturbing the ability of the transferee to recognize theoriginal pattern.

It should be also noted that a memory may be transferred based on therelational-aspect neurons as well (neurons p through s in an example3100 in FIG. 31) rather than, or in addition to, the afferents (neuronsa through d). However, if the transferee relies on the relational-aspectneurons, then these should not be erased if the memory is to be retainedby the transferee.

Moreover, a new bank of relational-aspect learning neurons may be usedin association with the transferee in order to replay the patternlearned by the transferee, refine the transferee's learning oraccomplish any of the above discussed further processing, including eventransferring the memory again. This may fit well with the generality ofthe cortical fabric structure of the present disclosure. Such astructure is illustrated in FIG. 32. It should be noted that althoughthe same letters are used for the neurons in both cortical areas, theseare not the same neurons. This is merely a convenience and theidentification is local to the cortical area.

The diagram 3200 in FIG. 32 illustrates an example of the connectivityfor memory transfer. While the cortical areas are shown separately,these may also just be different areas within a common area or pointsalong a stretch of cortical fabric. In the example 3200, both corticalareas may use the same afferents. A diagram 3300 in FIG. 33 shows thatonce transfer is complete, there may be no dependence on the firstcortical area. An example diagram 3400 in FIG. 34 shows an extension totransfer and at the same time associate separately learned patterns froma first and second cortical area by a third cortical area (transfer andassociate).

Finally, in regards to memory transfer, there may be a number ofextensions and alternatives. Transfer to the second or other memory maybe achieved in conjunction with erasing of the original memory. This maybe done without impacting replay fidelity because replay can be achievedusing the relational aspect neurons.

If replay does trigger the original pattern match to fire and this isdesired but learning refinement is not desired at the same time asmemory transfer, then the input from layer 2 (referencing or patternmatching neuron(s)) to layer 3 (relational-aspect learning) may beblocked by inhibitory connections. Specifically, pre-synaptic inhibitionof layer 2 to layer 3 synapses may be driven by either prior layer 3output (delayed) or by prior layer 2 output (controlled), for example.Variations of this concept are possible. The main idea is to use a prioroutput in the procedure as a trigger to inhibit particular connectionslayer in the procedure.

It should be noted that the above principles can also be applied toalternatives to the pre-synaptic inhibition used during exposure tooriginal stimulus, to inhibit the layer 3 to layer 1 connections byprior layer 1 output, as described above. Variations might include atrigger from the stimulus directly instead of layer 1 (afferents) orpost-synaptic inhibition from the stimulus or layer 1. In either ofthese cases, delay may be used to bring the time frame of the priorsignal in the procedure up to the time of the procedural step to beinhibited.

It should be noted that precise delay may not be necessarily required ifthe inhibitory impact is on a relatively large time scale (causal but,for example, having a decaying impact with a long time constant). Evenif the synaptic inhibition has a short time constant, resonatinginhibitory inter-neurons might be used to maintain the inhibition for alonger time window. Nevertheless, if the time window of inhibition isnarrow, time precision may be more advantageous. The time delay may alsobe learned (i.e., by the inhibitory neuron or circuit, even in anunsupervised learning) by using a learning rule that cross-correlatesthe relational-aspect learning firing and the causal afferent firing,thereby raising the weight of the synaptic connections with delays thatalign those firings.

FIG. 35 illustrates example operations 3500 for neural component memorytransfer in accordance with certain aspects of the present disclosure.At 3502, a pattern in a plurality of afferent neuron outputs may bereferenced with one or more referencing neurons. At 3504, one or morefirst relational aspects between the pattern in the plurality ofafferent neuron outputs and an output of the one or more referencingneurons may be matched with one or more first relational aspect neurons.At 3506, the pattern may be transferred to one or more transfereeneurons by inducing the plurality of afferent neurons to output a firstsubstantially similar pattern as the referenced pattern by the one ormore referencing neurons.

FIG. 36 illustrates example operations 3600 for neural associativelearning in accordance with certain aspects of the present disclosure.At 3602, a first pattern in a set of one or more inputs may bereferenced with a first stimulus. At 3604, a relational aspect betweenone or more elements of the first pattern in the set of inputs andreferencing of the first pattern may be learned. At 3606, a secondpattern in the set of one or more inputs may be referenced with a secondstimulus. At 3608, a relational aspect between one or more elements ofthe second pattern in the set of inputs and referencing of the secondpattern may be learned. At 3610, the first and second patterns in theset of inputs may be replayed using the learned relational aspectswithout the first and second stimuli. At 3612, the first and secondpatterns may be associated based on the replay.

Pattern Completion and Pattern Separation

Pattern completion is a process in which the system has previously beenexposed to an original stimulus that evoked an original pattern inafferents and then later, the system may be exposed to a partialstimulus that would evoke only a part of the original pattern exceptthat the replay method completes the pattern. In other words, theability of pattern completion may represent the ability to respond to adegraded input with a complete response.

The present disclosure provides a method of pattern completion. In orderto achieve pattern completion, a reference may be required (or patternlearner/matcher) that matches or references the degraded pattern ascorresponding to the original pattern. FIG. 37 illustrates a network3700 with several afferents 3702 and two references (or pattern-learningneurons) 3704, 3706. The original (full) pattern 3708 is alsoillustrated in FIG. 37. In an aspect, the neuron 3704 may reference thispattern (or has learned this pattern). Furthermore, the neuron 3704 mayalso reference (match) at least the degraded pattern 3710. In addition,all elements of the pattern may be learned by relational aspect neurons,as illustrated by the spikes 3712. The degraded input 3714 is alsoillustrated, and it may satisfy the minimum elements required for thereference, so the neuron 3704 may also fire on the degraded input 3714(not merely on the complete input 3708).

As per the descriptions above, the reference (match) output (the neuron3704 in FIG. 37) may be then input to the relational aspect layer whichmay cause the relational aspect neurons to fire. Due to therelational-neuron-to-afferent-neuron pre-synaptic inhibitionconnectivity described above, the elements of the original pattern thatare already in the degraded pattern may be suppressed from replay by theinhibitory circuits. However, since missing elements are not in thedegraded pattern, they may have no inhibitory feedback and thus may bereplayed. Thus, the pattern may be completed by the replay circuit, asillustrated in FIG. 38. It should be noted that the completion part maybe replayed at a delayed time (spikes 3802) relative to the degraded butexisting part (spikes 3804). These parts may be aligned by insertingdelay for the degraded but existing part so that they can be realigned(spikes 3806).

An alternative method of pattern completion can be to remove, suppressor overcome the pre-synaptic inhibitory circuits described above. As aresult, the degraded pattern may occur first. Then, at a delayed time,the complete pattern may be replayed, as illustrated in FIG. 39.

Another alternative method of pattern completion can be if the reference(match) fires before the end of the pattern. This is the purpose ofshowing the neuron 3706 in FIG. 37. It can be noticed that the neuron3706 fires mid-pattern (e.g., spikes 3902, 3904 in FIG. 39, and a spike4002 in FIG. 40). This may be because it recognizes the early part ofthe degraded pattern. In this case, the missing part occurring after thereference (the spike 4002 in FIG. 40) may be replayed with the degradedpart of the afferents, as illustrated in FIG. 40. This may be becausethe relational-aspect neurons can respond to the neuron 3706 and thusmay induce the afferents to fire after the neuron 3706 fires (but notbefore).

Finally, larger and more general pattern completion may be alsopossible. What is meant by larger is that the pattern being produced canbe longer in duration than the delay line range (e.g., of dendriticdelay range), so the pattern may be learned/referenced by multiplereference neurons in a sequence. What is meant by more general ispatterns with multiple spikes per afferent. An explanation is providedfurther below in the discussion of hierarchical replay.

A related but different process can be the process of patternseparation. This may represent the ability to modify similarstored/learned patterns to increase the difference between them andimprove distinction in recognizing stimuli. The present disclosure iscompatible with pattern separation because lateral inhibition at thereference layer may be used to separate reference layer neuron learningand thus separate stored patterns. Separation may occur duringrelational-aspect learning as well because this learning depends on arelation with the reference layer and the afferents. It should be notedthat if similar patterns cause confusion in reference firing, this canthus negatively impact the relational aspect learning, which may therebyseparate or suppress learning of similar aspects. A similar effect mayoccur if multiple references (corresponding to multiple patterns) firefor a single original pattern during relational-aspect learning.

FIG. 41 illustrates example operations 4100 for neural patterncompletion in accordance with certain aspects of the present disclosure.At 4102, a first pattern in a set of one or more inputs may bereferenced with a first stimulus. At 4104, a relational aspect betweenone or more elements of the first pattern in the set of inputs andreferencing of first pattern may be learned. At 4106, a second patternin the set of one or more inputs may be referenced with a secondstimulus, wherein the second pattern may comprise a degraded version ofthe first pattern. At 4108, at least one element of the first patternmissing or being degraded from the second pattern may be replayed inresponse to exposure to at least one of the second pattern or the secondstimulus.

FIG. 42 illustrates example operations 4200 for neural patternseparation in accordance with certain aspects of the present disclosure.At 4202, a first pattern in a set of one or more inputs may bereferenced with one or more referencing neurons. At 4204, a firstrelational aspect between one or more elements of the first pattern andreferencing of the first pattern may be learned. At 4206, a secondpattern in the set of one or more inputs may be referenced with the oneor more referencing neurons, wherein the second pattern may be similarto the first pattern. At 4208, a second relational aspect between one ormore elements of the second pattern and referencing of the secondpattern may be learned. At 4210, at least one of the first pattern orthe second pattern may be modified to increase a difference between thefirst and second patterns. At 4212, after the modification and using theone or more referencing neurons, the first pattern may be referencedwith a first stimulus and the second pattern may be referenced with asecond stimulus, wherein the first stimulus may be distinct from thesecond stimulus.

Pattern Comparison and Generalization

Certain aspects of the present disclosure support methods of patterncomparison. Pattern comparison represents the ability to compare twostored or learned patterns. A pattern may not need necessarily to bestored in one reference (pattern-learning) neuron, but it may be storedby the relational-aspect population of neurons or by multiple referenceneurons or a combination thereof. For example, there may be two or moresuch stored patterns that are to be compared offline (i.e., without theoriginal stimuli), or there may be one stored pattern and one that maybe occurring currently due to stimulus. The present disclosure providesmethods of comparing these.

FIG. 43 illustrates an example of neural connectivity diagram 4300 inaccordance with certain aspects of the present disclosure. In an aspect,the neuron x may be a reference for a first pattern and neuron y may bea reference for a second pattern, while relational-aspect learningneurons p through s have learned replay of both patterns, as describedabove. In order to compare the two patterns, controls may be used toinduce one or the other of neurons x and y to fire and invoke replay ofthe corresponding pattern. Further, it may be checked if that pattern inthe afferents (a through d) will be matched by (i) the correspondingneuron or (ii) the neuron corresponding to the other pattern, (iii)both, or (iv) neither. Thus, stored patterns can be compared in anoffline manner. Similarly, a stored pattern can be compared to a currentpattern in afferents (currently occurring by stimulus to afferents).

Furthermore, pattern comparison can be facilitated by lateralinhibition. In an aspect, neurons x and y may laterally inhibit eachother (not shown in FIG. 43). If a pattern similar to the patternsrecognized/referenced by neurons x and y is played/replayed, and if theneuron x fires first, it will inhibit neuron y from firing. In a sense,the first/best match may prevent the other. In contrast, if no matchoccurs, there may be no suppression and the faintest match may beencouraged. The better the match, the less may be the delay until neuronx or y (as the case may be) fires.

If two patterns are not the same but similar, it may be desirable togeneralize this issue. The generalization may occur at a higher layer orat the same layer. In the diagram 4300, the neuron t may berepresentative of a higher layer neuron while the neuron z may berepresentative of a neuron at the same layer (as the pattern learningneurons). It should be noted that in neither case (neither t nor z) arerequired to be connected to the relational aspect learning neurons(neurons p through s) that facilitate replay. In addition, in the caseof higher-layer, the neuron t may not need to be directly connected toneurons x and y, but it may be at an even higher layer (more indirect).

A search may be performed by replaying a series of patterns. As a matchfor a target pattern gets closer, the reference neuron corresponding tothe target pattern may become more and more likely to fire (or increaseits firing rate, for example).

Generalization may occur online (during play) or offline (with replay).By playing or replaying the patterns, the generalizing neuron may learnto fire for both patterns. The key can be that firing is not mademutually exclusive, such as by inhibition. For example, in the case ofneuron z, the firing of neuron x or y should not inhibit neuron z fromfiring if it is desired for neuron z to generalize the patterns alreadyreferenced by neurons x and y.

FIG. 44 illustrates example operations 4400 for neural comparison inaccordance with certain aspects of the present disclosure. At 4402, afirst pattern in a set of one or more inputs may be referenced with afirst stimulus. At 4404, a relational aspect between one or moreelements of the first pattern in the set of inputs and referencing ofthe first pattern may be learned. At 4406, a second pattern in the setof one or more inputs may be referenced with a second stimulus. At 4408,the first pattern may be replayed. At 4410, the first pattern may becompared with the second pattern based on the replay and referencing ofthe first and second patterns.

FIG. 45 illustrates example operations 4500 for neural patterngeneralization in accordance with certain aspects of the presentdisclosure. At 4502, a first pattern in a set of one or more inputs maybe referenced with a first stimulus. At 4504, a relational aspectbetween one or more elements of the first pattern in the set of inputsand referencing of the first pattern may be learned. At 4506, a secondpattern in the set of one or more inputs may be referenced with a secondstimulus. At 4508, a relational aspect between one or more elements ofthe second pattern in the set of inputs and referencing of the secondpattern may be learned. At 4510, at least one of the first pattern orthe second pattern may be replayed without the first and second stimuli.At 4512, a generalization of the first and second patterns may belearned based on the replay.

Horizontal (Auto) Association

In an aspect, there may be a sensory stimulus, such as seeing aparticular face, which may cause a particular afferent pattern. A firstpattern-learning neuron (e.g., in a first cortical area: visual) maylearn this pattern according to the above methods. However, there may bealso a simultaneous (or even just close-in-time) stimulus in anothermodality, e.g., a loud sound pattern that may be learned by a secondpattern-learning neuron (e.g., in a second cortical area: auditory). Inan aspect, the first pattern-learning neuron may not be connected tothis second (auditory) sensory modality input so it may not learn thissound as part of the pattern. This situation is illustrated in FIG. 46.

It should be noted that cortical area pattern learning neurons 4602 maynot be connected to one another's afferents. According to therelational-aspect learning neurons of the present disclosure, therelational-aspect learning neurons 4604 may be paired with afferentsfrom both modalities. Moreover, they may receive input from both thevisual and auditory pattern-matching/learning neurons. Thus, therelational-aspect learning neurons may be connected across to efferentsof other cortical areas, as represented in FIG. 46 by a dendriticprocess 4606 from second cortical area relational-aspect neurons 4608 toaxons of first cortical area reference neurons 4610. Further, firstcortical area afferents 4612 and second cortical area afferents 4614 maybe connected to third cortical area afferents, as illustrated in FIG.46. Although the neural connectivity diagram 4600 from FIG. 46illustrates only one such horizontal connection and only in onedirection, horizontal connections may be made by one or more (even each)of the relational-learning aspect neurons from either cortical area.

It should be noted that by triggering replay, even though the visual andauditory patterns were learned by different neurons, the replay maycomprise both visual and auditory patterns. In an aspect, off-linelearning or memory transfer may associate both and learn the combinedpattern (e.g., a transferee pattern-learning neuron may match thecombination of visual and auditory input).

Learning Speed and Referencing

The advantage of being able to improve the learning of a pattern after astimulus is no longer available was discussed above in relation tolearning refinement. However, methods of the present disclosure may haveeven greater potential. In the general discussions above, a patternlearning (or at least matching) neuron was used in the second layer ofthe cortical fabric. Technically, this pattern learning neuron may bereally a reference for relational aspect learning. What is happening canbe that the relational aspect learning is using the output of thepattern learning neuron to relate the individual elements of the patternfrom each afferent. Whether that reference is a pattern learning ormatching neuron is not important for that purpose. In other words, thereferencing neuron could be a neuron other than a pattern learning (ormatching) neuron. In an aspect, this neuron may not even be connected tothe afferents. Referencing is illustrated by the neuron y of a neuralconnectivity diagram 4700 in FIG. 47.

In one aspect of the present disclosure, a pattern learning neuron maybe used as a reference, while stimulus may be available as generallydiscussed except to only learn for as long as is required to obtain aconsistent firing of the pattern-learning neuron (and afferents, ifthere is a strong feedback) with the pattern but not necessarily longenough to develop learning to a point the neuron can distinguish thatpattern from others (or just from other similar patterns). In otherwords, learning during stimulus may be very fast since there may be noneed to really refine the pattern matching or distinguishing capability.This may be done offline (without the stimulus) using the same neuron(refining the learning) or transfer to another neuron with or withoutassociation to other concepts/inputs.

In another aspect, one of the afferents may be used as the reference.This may work well when that afferent is consistently associated withthe particular pattern (and, for example, does not fire when otherpatterns are present). Indeed, if a particular afferent is already agood indicator of a pattern, there may be less motivation to learn thepattern. But, this may not necessarily be so. For example, a particularhat might be a particularly unique or distinguishing feature of aperson. However, that doesn't obviate the importance of recognizing theperson's face. If an afferent is used as the reference, there may be noneed to connect other afferents to that afferent. The neuron y in FIG.47 might represent such situation. It should be noted that arelational-aspect learning neuron could be also used as the reference.

In yet another aspect, it may be possible to use a separate neuron otherthan afferents or pattern matching/learning neurons such as a neuronfrom another part of a cortical array or neural network or a neuron thatis periodically excited using oscillation or otherwise controlled tooccur with the occurrence of a stimulus, such as an attention marker. Itcan be supposed, for example, that attention can be fixed on a face. Avisual system may be receiving afferents with a pattern corresponding tothe face. An attentional circuit may provide a reference to the visualsystem coincident with the attention on this face. The reference may bethen used by the relational aspect learning neurons to store informationto be able to replay once the stimulus (face) is gone from view. Inorder to replay, the attentional circuit may trigger the reference that,as described above, triggers the replay via the original afferents. Thismay be then used for learning of the face pattern, learning refinement,transfer of the learning or memory or information, or association.

FIG. 48 illustrates example operations 4800 for neural componentlearning refinement and fast learning in accordance with certain aspectsof the present disclosure. At 4802, a pattern in a plurality of afferentneuron outputs may be referenced with one or more pattern learningneurons. At 4804, one or more relational aspects between the pattern inthe plurality of afferent neuron outputs and an output of one or morereferencing neurons may be matched with one or more relational aspectneurons. At 4806, one or more of the plurality of afferent neurons maybe induced to output a substantially similar pattern as the referencedpattern by the one or more referencing neurons. At 4808, learning by theone or more pattern learning neurons may be refined using the inducedsubstantially similar pattern.

Procedural Flow, Repeating Replay and Directing Flow

In an aspect of the present disclosure, inhibition can be used to directthe course of a procedure. The term procedure can be used to refer to aprocess such as relational-aspect learning with an original stimulus,replay, memory transfer, or learning refinement, and so on. A proceduremay be triggered by a particular control or input (e.g., stimulus) ormay merely be one state in an oscillation of the network. Regardless,once triggered, the process may be controlled either by the internaloperations (state transition based on prior activity of the network) orby external controls (outside of the local array or network area) or acombination thereof, as illustrated by a procedural flow 4900 in FIG.49.

An example of the internal control can be an oscillation betweenafferent firing, pattern neuron firing, relational-aspect neuron firing,afferent firing again, and so on in a loop driven by the prior firing(afferent firing may drive pattern neuron firing, and so on). An exampleof external control can be inducing the pattern neuron to fire due toexternal control signals (other neurons outside the array). Acombination may also be used so that initial activity may be triggeredby a control, but the subsequent oscillation that occurs may beself-caused. Variations on this may comprise self-triggering butcontrolled oscillation. Regardless, in addition to excitation,inhibition may be used to effect procedural flow (determine which stateoccurs next). It should be noted that, in the above description,specific inhibition (pre-synaptic) was used to prevent relational-aspectneurons from triggering replay during exposure to the original stimulus(driven either directly by the stimulus or by afferent firing andaffecting the connections from the relational-aspect learning neuronsback to the afferents).

However, this general idea may be applied in a number of alternate waysand to other procedures. First, the memory transfer process can beconsidered as an example of the latter. During replay for memorytransfer, the afferents may playback the pattern. The pattern may bematched by a pattern matching neuron (or reference) that may beconnected to relational-aspect learning neurons. Thus, the replay mayinvoke replay again. Due to the specific inhibition mentioned above,repeated replay may be blocked. This block may be removed if repeatedreplay is desired, or another control may be added to invoke repetition(e.g., periodic stimulation of the reference or pattern neuron).

However, repeat may not be desired and further, even the re-firing ofthe relational-aspect neurons may be undesired. A reason for this isthat learning refinement (of the relational-aspects) may not be desired(e.g., during memory transfer). To block this, a prior event (firing) inthe procedure may be used as a driver for inhibiting the undesiredevent. Specifically, an inhibitory interneuron may be connected toreceive input from the reference or pattern matching neuron and tooutput on the excitatory connection(s) between that same neuron and (to)relational-aspect neuron(s). By assigning a delay commensurate with thetime between steps in the procedural flow, the interneuron may block thecause of the undesired events at the right time. Thus, internalconnectivity may be designed to ensure the desired procedural flow forany particular procedure.

Hierarchical and Multi-Layer Replay and Multi-Part Pattern Completion

According to certain aspects of the present disclosure, the concept ofhierarchical and multi-layer replay can represent that of applying theconcept of the replay architecture described above at multiple layers ofa network and potentially replaying at one or more layers,hierarchically. What is meant by multiple layers may comprise neurons atvarying levels of generalization or abstraction in a network. What ismeant by hierarchical replay is that inducing replay at a particularpattern learning or reference layer (neurons) may then in turn inducereplay of the learned/referenced pattern(s) in the afferents of thosepattern learning or reference layer neurons. Thus, replay may be inducedfrom a top-down order in a layered network.

The hierarchical replay is described by way of example 5000 illustratedin FIG. 50. Neurons x, y, and z in FIG. 50 may be pattern learningneurons (or references). However, while neurons x and y may learn orreference patterns in afferents a and b, the neuron z may learn orreferences a pattern in neurons x and y. Similarly, neurons p and q maybe relational aspect neurons for the lower part of the network, whileneurons t and u may be relational aspect neurons for the upper part. Itshould be noted that replay of patterns at the lower layer may beinvoked by replay at the upper layer. In an aspect, replay at the upperlayer may be invoked by the firing of neuron z. For example, the firingsequence may proceed as follows: z→t, u→x,y→p, q→a,b, and so on.

With strategic inhibitory connectivity or excitatory boost, it may bepossible to achieve further processing goals such as larger patterncompletion. For pattern completion, the pattern matched by neuron x mayoccur due to external stimulus, and it may be desired that the patternmatched by neuron y to be replayed as a result. Since the patternmatched by neuron x occurs, neuron x may fire. By boosting thesensitivity or lowering the threshold of neuron z, this neuron may beinduced to fire as a result. Now, this may induce hierarchical replaydescribed above, with some important exceptions. First, the originalpattern of neuron x has already occurred. This may be used to inhibitreplay of pattern referenced by neuron x by inhibiting neuron x fromfurther firing (at least in the short-term). Second, since the patternof neuron y did not occur yet, the neuron y may not be inhibited fromfiring. Thus, neurons p and q may fire to invoke only replay of thepattern matched/referenced by neuron y. The example process can besummarized in an example flow chart 5100 in FIG. 51.

The hierarchical replay may provide a means for additional furtherprocessing, including a process that may be called “associativecompletion”. For example, a machine may be learning a sequence ofpatterns (e.g., a birdsong or speech) abstractly denoted by the orderedlist of parts: A, B, C. In an aspect, the part B may follow part A, andpart C may follow part B. Without loss of generality, the focus may beon one step in the association, for example on step A→B. In an aspect,the network illustrated in FIG. 50 may learn the patterns A and B atneurons x and y. Moreover, the order of A→B may be learned by neuron zsince it may learn the temporal aspects of the firing of neurons x and y(which reference A and B). If the machine begins by replaying pattern Aalone, this may be used to trigger replay of the associated B exactly asdescribed above for pattern completion (except that the first part wasalso a replay). By adding elements to the network, replay of C may beinvoked based on the replay of B, and so on. In effect, replay of asequence may be invoked step by step.

Now, an observant reader might ask: “won't B then cause replay of Ainstead of C or in addition to C”. That can be indeed possible unlessmodifications are made to avoid this, i.e., to maintain the forward flowof the sequence. One way to do this may be to inhibit the re-firing ofeach layer 2 neuron for a period after it has already fired. This periodmay correspond to duration between parts that are two-parts away fromone another (i.e., having one part in-between).

Now, the observant reader might ask: “won't that prevent replay of asequence such as A→A→B or A→B→A→C”. The answer is not necessarily. Ifthere is only one neuron to match part A, then this may be a problem.However, if multiple layer 2 neurons are allowed to learn pattern A,with lateral inhibition so that different neurons learn pattern A atdifferent points in the sequence, then this problem may be overcome.

Finally, control inputs may also be provided to limit replay to certainlevels of a network. For example, replay may be controlled to berestricted to upper layers (higher-layers of conceptual abstraction orpattern recognition) by inhibiting firing of lower layer referenceneurons.

FIG. 52 illustrates example operations 5200 for neural pattern sequencecompletion in accordance with certain aspects of the present disclosure.At 5202, each sequence of parts of a pattern in a set of one or morefirst layer neurons may be referenced with a second layer of referencingneurons. At 5204, a relational aspect between one or more elements ofthe pattern and the referencing of that sequence of parts of the patternmay be learned. At 5206, a pattern sequence in the second layer ofreferencing neurons may be referenced with a third layer of referencingneurons. At 5208, a relational aspect between one or more elements ofthe pattern sequence and the referencing of pattern sequence in thesecond layer of referencing neurons may be learned. At 5210, asubsequent part of the pattern in the first layer neurons may bereplayed upon producing a prior part of the pattern.

FIG. 53 illustrates example operations 5300 for neural patternhierarchical replay in accordance with certain aspects of the presentdisclosure. At 5302, each sequence of parts of a pattern in a set of oneor more first layer neurons may be referenced with a second layer ofreferencing neurons. At 5304, a relational aspect between one or moreelements of each pattern and the referencing of that sequence of partsof the pattern may be learned. At 5306, a pattern sequence in the secondlayer of referencing neurons may be referenced with a third layer ofreferencing neurons. At 5308, a relational aspect between one or moreelements of the pattern sequence and the referencing of the patternsequence in the second layer of referencing neurons may be learned. At5310, replay of the referencing of the pattern sequence in the secondlayer may be invoked based on the third layer of referencing neurons. At5312, that sequence of parts of the pattern in the first layer may bereplayed based on the invoking of replay of the referencing of thepattern sequence in the second layer.

FIG. 54 illustrates example operations 5400 for neural pattern sequencecompletion that may be performed without a hierarchy in accordance withcertain aspects of the present disclosure. At 5402, a plurality of partsof a pattern in a plurality of afferent neurons may be referenced with aplurality of referencing neurons. At 5404, one or more of the parts ofthe pattern may be related, with one or more relational aspect neurons,to a subset of the referencing neurons based on a delay between theafferent neurons and the one or more relational aspect neurons beingsmaller than a first value. At 5406, one or more remaining parts of thepattern may be related, with the one or more relational aspect neurons,to the subset of referencing neurons based on the delay being largerthan a second value. At 5408, replay of the one or more remaining partsof the pattern may be induced by the subset of referencing neurons basedon firing elements of the one or more parts of the pattern by theafferent neurons.

FIG. 55 illustrates an example software implementation 5500 of theaforementioned methods for neural component replay, learning refinement,memory transfer, associative learning, pattern comparison, patterncompletion, pattern separation, pattern generalization, pattern sequencecompletion with a hierarchy, and pattern hierarchical replay using ageneral-purpose processor 5502 in accordance with certain aspects of thepresent disclosure. Weights and delays associated with each connection(synapse) of a computational network (neural network) may be stored in amemory block 5504, while instructions related to the aforementionedmethods being executed at the general-purpose processor 5502 may beloaded from a program memory 5506.

In one aspect of the present disclosure, the instructions loaded intothe general-purpose processor 5502 may comprise code for referencing apattern in a plurality of afferent neuron outputs with one or morepattern learning neurons, code for matching one or more relationalaspects between the pattern in the plurality of afferent neuron outputsand an output of one or more referencing neurons with one or morerelational aspect neurons, code for inducing one or more of theplurality of afferent neurons to output a substantially similar patternas the referenced pattern by the one or more referencing neurons, codefor refining learning by the one or more pattern learning neurons usingthe induced substantially similar pattern, code for transferring thepattern to one or more transferee neurons by inducing the plurality ofafferent neurons to output a first substantially similar pattern as thereferenced pattern by the one or more referencing neurons, and code forlearning one or more relational aspects between the pattern in theplurality of afferent neuron outputs and an output of the one or morereferencing neurons with one or more relational aspect neurons usingstructural plasticity. In another aspect, the instructions loaded intothe general-purpose processor 5502 may comprise code for learning asubset of a pattern in a set of inputs with a stimulus, code forlearning a relational aspect between elements of the pattern and thesubset of the pattern, code for replaying the pattern in the set ofinputs using the learned relational aspect without the stimulus, andcode for refining learning of the pattern in the set of inputs withoutthe stimulus.

In yet another aspect, the instructions loaded into the general-purposeprocessor 5502 may comprise code for referencing a first pattern in aset of one or more inputs with a first stimulus, code for learning arelational aspect between one or more elements of the first pattern inthe set of inputs and referencing of the first pattern, code forreferencing a second pattern in the set of one or more inputs with asecond stimulus, code for learning a relational aspect between one ormore elements of the second pattern in the set of inputs and referencingof the second pattern, code for replaying the first and second patternsin the set of inputs using the learned relational aspects without thefirst and second stimuli, code for associating the first and secondpatterns based on the replay, code for comparing the first pattern withthe second pattern, code for replaying at least one element of the firstpattern missing or being degraded from the second pattern in response toexposure to at least one of the second pattern or the second stimulus,code for modifying at least one of the first pattern or the secondpattern to increase a difference between the first and second patterns,code for referencing, after the modification using the one or morereferencing neurons, the first pattern with a first stimulus and thesecond pattern with a second stimulus, wherein the first stimulus may bedistinct from the second stimulus, and code for learning ageneralization of the first and second patterns.

In yet another aspect, the instructions loaded into the general-purposeprocessor 5502 may comprise code for referencing each sequence of partsof a pattern in a set of one or more first layer neurons with a secondlayer of referencing neurons, code for learning a relational aspectbetween one or more elements of each pattern and the referencing of thatsequence of parts of the pattern, code for referencing a patternsequence in the second layer of referencing neurons with a third layerof referencing neurons, code for learning a relational aspect betweenone or more elements of the pattern sequence and the referencing of thepattern sequence in the second layer of referencing neurons, code forinvoking replay of the referencing of the pattern sequence in the secondlayer based on the third layer of referencing neurons, code forreplaying that sequence of parts of the pattern in the first layer basedon the invoking of replay of the referencing of the pattern sequence inthe second layer, and code for replaying a subsequent part of thepattern in the first layer neurons upon producing a prior part of thepattern.

In yet another aspect, the instructions loaded into the general-purposeprocessor 5502 may comprise code for referencing a plurality of parts ofa pattern in a plurality of afferent neurons with a plurality ofreferencing neurons, code for relating, with one or more relationalaspect neurons, one or more of the parts of the pattern to a subset ofthe referencing neurons based on a delay between the afferent neuronsand the one or more relational aspect neurons being smaller than a firstvalue, code for relating, with the one or more relational aspectneurons, one or more remaining parts of the pattern to the subset ofreferencing neurons based on the delay being larger than a second value,and code for inducing replay of the one or more remaining parts of thepattern by the subset of referencing neurons based on firing elements ofthe one or more parts of the pattern by the afferent neurons.

FIG. 56 illustrates an example implementation 5600 of the aforementionedmethods for neural component replay, learning refinement, memorytransfer, associative learning, pattern comparison, pattern completion,pattern separation, pattern generalization, pattern sequence completionwith a hierarchy, and pattern hierarchical replay, where a memory 5602can be interfaced via an interconnection network 5604 with individual(distributed) processing units (neural processors) 5606 of acomputational network (neural network) in accordance with certainaspects of the present disclosure. One or more weights and delaysassociated with one or more connections (synapses) of the computationalnetwork (neural network) may be loaded from the memory 5602 viaconnection(s) of the interconnection network 5604 into each processingunit (neural processor) 5606.

In one aspect of the present disclosure, the processing unit 5606 may beconfigured to reference a pattern in a plurality of afferent neuronoutputs with one or more pattern learning neurons, match one or morerelational aspects between the pattern in the plurality of afferentneuron outputs and an output of one or more referencing neurons with oneor more relational aspect neurons, induce one or more of the pluralityof afferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons, refinelearning by the one or more pattern learning neurons using the inducedsubstantially similar pattern, transfer the pattern to one or moretransferee neurons by inducing the plurality of afferent neurons tooutput a first substantially similar pattern as the referenced patternby the one or more referencing neurons, and learn one or more relationalaspects between the pattern in the plurality of afferent neuron outputsand an output of the one or more referencing neurons with one or morerelational aspect neurons using structural plasticity. In anotheraspect, the processing unit 5606 may be configured to learn a subset ofa pattern in a set of inputs with a stimulus, learn a relational aspectbetween elements of the pattern and the subset of the pattern, replaythe pattern in the set of inputs using the learned relational aspectwithout the stimulus, and refine learning of the pattern in the set ofinputs without the stimulus.

In yet another aspect, the processing unit 5606 may be configured toreference a first pattern in a set of one or more inputs with a firststimulus, learn a relational aspect between one or more elements of thefirst pattern in the set of inputs and referencing of the first pattern,reference a second pattern in the set of one or more inputs with asecond stimulus, learn a relational aspect between one or more elementsof the second pattern in the set of inputs and referencing of the secondpattern, replay the first and second patterns in the set of inputs usingthe learned relational aspects without the first and second stimuli,associate the first and second patterns based on the replay, compare thefirst pattern with the second pattern, replay at least one element ofthe first pattern missing or being degraded from the second pattern inresponse to exposure to at least one of the second pattern or the secondstimulus, modify at least one of the first pattern or the second patternto increase a difference between the first and second patterns,reference, after the modification using the one or more referencingneurons, the first pattern with a first stimulus and the second patternwith a second stimulus, wherein the first stimulus may be distinct fromthe second stimulus, and learn a generalization of the first and secondpatterns.

In yet another aspect, the processing unit 5606 may be configured toreference each sequence of parts of a pattern in a set of one or morefirst layer neurons with a second layer of referencing neurons, learn arelational aspect between one or more elements of each pattern and thereferencing of that sequence of parts of the pattern, reference apattern sequence in the second layer of referencing neurons with a thirdlayer of referencing neurons, learn a relational aspect between one ormore elements of the pattern sequence and the referencing of the patternsequence in the second layer of referencing neurons, invoke replay ofthe referencing of the pattern sequence in the second layer based on thethird layer of referencing neurons, replay that sequence of parts of thepattern in the first layer based on the invoking of replay of thereferencing of the pattern sequence in the second layer, and replay asubsequent part of the pattern in the first layer neurons upon producinga prior part of the pattern.

In yet another aspect, the processing unit 5606 may be configured toreference a plurality of parts of a pattern in a plurality of afferentneurons with a plurality of referencing neurons, relate, with one ormore relational aspect neurons, one or more of the parts of the patternto a subset of the referencing neurons based on a delay between theafferent neurons and the one or more relational aspect neurons beingsmaller than a first value, relate, with the one or more relationalaspect neurons, one or more remaining parts of the pattern to the subsetof referencing neurons based on the delay being larger than a secondvalue, and induce replay of the one or more remaining parts of thepattern by the subset of referencing neurons based on firing elements ofthe one or more parts of the pattern by the afferent neurons.

FIG. 57 illustrates an example implementation 5700 of the aforementionedmethods for neural temporal coding based on distributed weight/delaymemories 5702 and distributed processing units (neural processors) 5704in accordance with certain aspects of the present disclosure. Asillustrated in FIG. 57, one memory bank 5702 may be directly interfacedwith one processing unit 5704 of a computational network (neuralnetwork), wherein that memory bank 5702 may store one or more weightsand delays of one or more connections (synapses) associated with thatprocessing unit (neural processor) 5704.

In one aspect of the present disclosure, the processing unit 5704 may beconfigured to reference a pattern in a plurality of afferent neuronoutputs with one or more pattern learning neurons, match one or morerelational aspects between the pattern in the plurality of afferentneuron outputs and an output of one or more referencing neurons with oneor more relational aspect neurons, induce one or more of the pluralityof afferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons, refinelearning by the one or more pattern learning neurons using the inducedsubstantially similar pattern, transfer the pattern to one or moretransferee neurons by inducing the plurality of afferent neurons tooutput a first substantially similar pattern as the referenced patternby the one or more referencing neurons, and learn one or more relationalaspects between the pattern in the plurality of afferent neuron outputsand an output of the one or more referencing neurons with one or morerelational aspect neurons using structural plasticity. In anotheraspect, the processing unit 5704 may be configured to learn a subset ofa pattern in a set of inputs with a stimulus, learn a relational aspectbetween elements of the pattern and the subset of the pattern, replaythe pattern in the set of inputs using the learned relational aspectwithout the stimulus, and refine learning of the pattern in the set ofinputs without the stimulus.

In yet another aspect, the processing unit 5704 may be configured toreference a first pattern in a set of one or more inputs with a firststimulus, learn a relational aspect between one or more elements of thefirst pattern in the set of inputs and referencing of the first pattern,reference a second pattern in the set of one or more inputs with asecond stimulus, learn a relational aspect between one or more elementsof the second pattern in the set of inputs and referencing of the secondpattern, replay the first and second patterns in the set of inputs usingthe learned relational aspects without the first and second stimuli,associate the first and second patterns based on the replay, compare thefirst pattern with the second pattern, replay at least one element ofthe first pattern missing or being degraded from the second pattern inresponse to exposure to at least one of the second pattern or the secondstimulus, modify at least one of the first pattern or the second patternto increase a difference between the first and second patterns,reference, after the modification using the one or more referencingneurons, the first pattern with a first stimulus and the second patternwith a second stimulus, wherein the first stimulus may be distinct fromthe second stimulus, and learn a generalization of the first and secondpatterns.

In yet another aspect, the processing unit 5704 may be configured toreference each sequence of parts of a pattern in a set of one or morefirst layer neurons with a second layer of referencing neurons, learn arelational aspect between one or more elements of each pattern and thereferencing of that sequence of parts of the pattern, reference apattern sequence in the second layer of referencing neurons with a thirdlayer of referencing neurons, learn a relational aspect between one ormore elements of the pattern sequence and the referencing of the patternsequence in the second layer of referencing neurons, invoke replay ofthe referencing of the pattern sequence in the second layer based on thethird layer of referencing neurons, replay that sequence of parts of thepattern in the first layer based on the invoking of replay of thereferencing of the pattern sequence in the second layer, and replay asubsequent part of the pattern in the first layer neurons upon producinga prior part of the pattern.

In yet another aspect, the processing unit 5704 may be configured toreference a plurality of parts of a pattern in a plurality of afferentneurons with a plurality of referencing neurons, relate, with one ormore relational aspect neurons, one or more of the parts of the patternto a subset of the referencing neurons based on a delay between theafferent neurons and the one or more relational aspect neurons beingsmaller than a first value, relate, with the one or more relationalaspect neurons, one or more remaining parts of the pattern to the subsetof referencing neurons based on the delay being larger than a secondvalue, and induce replay of the one or more remaining parts of thepattern by the subset of referencing neurons based on firing elements ofthe one or more parts of the pattern by the afferent neurons.

It should be understood that while particular terms are used to describecomponents in the present disclosure, such as neuron or synapse, theconcepts of the disclosure can be implemented in equivalent alternateforms with equivalent units or elements.

Although the embodiments herein are shown for spiking neural networks,the use of these concepts to other neural network types including butnot limited to rate-based neural networks is also within the scope ofthe present disclosure.

The various operations of methods described above may be performed byany suitable means capable of performing the corresponding functions.The means may include various hardware and/or software component(s)and/or module(s), including, but not limited to a circuit, anapplication specific integrate circuit (ASIC), or processor. Generally,where there are operations illustrated in Figures, those operations mayhave corresponding counterpart means-plus-function components withsimilar numbering. For example, operations 800, 1600, 2300, 2400, 2900,3500, 3600, 4100, 4200, 4400, 4500, 4800, 5200, 5300 and 5400illustrated in FIGS. 8, 16, 23, 24, 29, 35, 36, 41, 42, 44, 45, 48, 52,53 and 54 correspond to components 800A, 1600A, 2300A, 2400A, 2900A,3500A, 3600A, 4100A, 4200, 4400A, 4500A, 4800A, 5200A, 5300A and 5400Aillustrated in FIGS. 8A, 16A, 23A, 24A, 29A, 35A, 36A, 41A, 42A, 44A,45A, 48A, 52A, 53A and 54A.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in any form of storage medium that is knownin the art. Some examples of storage media that may be used includerandom access memory (RAM), read only memory (ROM), flash memory, EPROMmemory, EEPROM memory, registers, a hard disk, a removable disk, aCD-ROM and so forth. A software module may comprise a singleinstruction, or many instructions, and may be distributed over severaldifferent code segments, among different programs, and across multiplestorage media. A storage medium may be coupled to a processor such thatthe processor can read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in software, thefunctions may be stored or transmitted over as one or more instructionsor code on a computer-readable medium. Computer-readable media includeboth computer storage media and communication media including any mediumthat facilitates transfer of a computer program from one place toanother. A storage medium may be any available medium that can beaccessed by a computer. By way of example, and not limitation, suchcomputer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium that can be used to carry or store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Also, any connection is properly termed acomputer-readable medium. For example, if the software is transmittedfrom a website, server, or other remote source using a coaxial cable,fiber optic cable, twisted pair, digital subscriber line (DSL), orwireless technologies such as infrared (IR), radio, and microwave, thenthe coaxial cable, fiber optic cable, twisted pair, DSL, or wirelesstechnologies such as infrared, radio, and microwave are included in thedefinition of medium. Disk and disc, as used herein, include compactdisc (CD), laser disc, optical disc, digital versatile disc (DVD),floppy disk, and Blu-ray® disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers. Thus, insome aspects computer-readable media may comprise non-transitorycomputer-readable media (e.g., tangible media). In addition, for otheraspects computer-readable media may comprise transitorycomputer-readable media (e.g., a signal). Combinations of the aboveshould also be included within the scope of computer-readable media.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

While the foregoing is directed to aspects of the present disclosure,other and further aspects of the disclosure may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1. A method of neural component replay, comprising: referencing apattern in a plurality of afferent neuron outputs with one or morereferencing neurons; learning one or more relational aspects between thepattern in the plurality of afferent neuron outputs and an output of theone or more referencing neurons with one or more relational aspectneurons using structural plasticity; and inducing one or more of theplurality of afferent neurons to output a substantially similar patternas the referenced pattern by the one or more referencing neurons.
 2. Themethod of claim 1, wherein: an unsupervised learning is used to learn atleast one of the pattern, the referencing, or the relational-aspects,the unsupervised learning comprises spike timing dependent plasticityand the structural plasticity controlling weights, delays, and inputs ofsynapses associated with neurons, and the structural plasticity isconstrained by structural relationships between the neurons or theirprocesses comprising at least one of a variability of delay, a range ofdelay, inputs, a weight range, or a weight variability.
 3. The method ofclaim 1, wherein a constraint model for the structural plasticitycomprises limitations on at least one of: delays corresponding toparallel or cruciate dendritic arbors and axonal processes, inputneurons based on proximity or extent of dendritic arbors or axonalprocesses, or delays relating to basal or apical dendritic processes. 4.The method of claim 1, wherein: at least one of each of the relationalaspect neurons or each of the referencing neurons is potentiallyconnected to a plurality of pattern-matching neurons, the potentialconnection comprises a synaptic connection which is reusable, and thesynapse is reused with a new delay based on a weight of the synapticconnection and structural constraints of neurons or processes of theneurons the synapse connects.
 5. The method of claim 1, wherein: atleast one of each of the relational aspect neurons or each of thereferencing neurons is potentially connected to a plurality ofpattern-matching neurons, the potential connection comprises a synapticconnection with a variable delay, and the delay is varied based on aweight of the synaptic connection and structural constraints of neuronsor processes of the neurons the synapse connects.
 6. The method of claim1, wherein: at least one of each of the relational aspect neurons oreach of the referencing neurons is potentially connected to a pluralityof pattern-matching neurons by a plurality of connections, the potentialconnection comprises a synaptic connection with a delay, and theplurality of connections have a range of the delays being set based onstructural constraints of neurons or processes of the neurons thesynapse connects.
 7. The method of claim 1, wherein the one or morerelational aspect neurons have dendritic processes parallel to referenceneuron axonal processes allowing a range of synaptic connectionparameters including variable delay between neurons.
 8. The method ofclaim 1, wherein the one or more relational aspect neurons areassociated with distal dendritic processes connecting the one or morerelational aspect neurons to afferent axons with a larger delay thanapical processes.
 9. The method of claim 1, wherein the one or morerelational aspect neurons have axons extending vertically to dendritesof the afferent neurons incurring a delay to connect thereto.
 10. Themethod of claim 1, wherein the one or more referencing neurons havecruciate dendritic processes that spread out to connect to multipleafferent neuron axons with a varied delay profile.
 11. The method ofclaim 1, wherein the one or more referencing neurons are laterallyinhibited by other referencing neurons neighboring the one or morereferencing neurons.
 12. The method of claim 1, wherein a delay of aninput from the afferent neurons to a subset of the one or morerelational aspect neurons paired with the afferent neurons comprises aconstrained common delay.
 13. The method of claim 1, wherein feedbackconnections from the relational aspect neurons to the afferent neuronsare pre-synaptically inhibited by at least one of: an inhibitoryfeedback from neighboring afferents, an inhibitory feedback from thereferencing neurons, or one or more inter-neurons receiving an inputfrom one or more afferents, the one or more relational aspect neurons,or the one or more referencing neurons.
 14. The method of claim 1,wherein a connectivity aspect between one of the afferent neurons andone of the relational aspect neurons paired with that one afferentneuron is learned in a development process.
 15. The method of claim 14,wherein the connectivity aspect comprises a delay.
 16. An apparatus forneural component replay, comprising: a first circuit configured toreference a pattern in a plurality of afferent neuron outputs with oneor more referencing neurons; a second circuit configured to learn one ormore relational aspects between the pattern in the plurality of afferentneuron outputs and an output of the one or more referencing neurons withone or more relational aspect neurons using structural plasticity; and athird circuit configured to induce one or more of the plurality ofafferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons.
 17. Theapparatus of claim 16, wherein: an unsupervised learning is used tolearn at least one of the pattern, the referencing, or therelational-aspects, the unsupervised learning comprises spike timingdependent plasticity and the structural plasticity controlling weights,delays, and inputs of synapses associated with neurons, and thestructural plasticity is constrained by structural relationships betweenthe neurons or their processes comprising at least one of a variabilityof delay, a range of delay, inputs, a weight range, or a weightvariability.
 18. The apparatus of claim 16, wherein a constraint modelfor the structural plasticity comprises limitations on at least one of:delays corresponding to parallel or cruciate dendritic arbors and axonalprocesses, input neurons based on proximity or extent of dendriticarbors or axonal processes, or delays relating to basal or apicaldendritic processes.
 19. The apparatus of claim 16, wherein: at leastone of each of the relational aspect neurons or each of the referencingneurons is potentially connected to a plurality of pattern-matchingneurons, the potential connection comprises a synaptic connection whichis reusable, and the synapse is reused with a new delay based on aweight of the synaptic connection and structural constraints of neuronsor processes of the neurons the synapse connects.
 20. The apparatus ofclaim 16, wherein: at least one of each of the relational aspect neuronsor each of the referencing neurons is potentially connected to aplurality of pattern-matching neurons, the potential connectioncomprises a synaptic connection with a variable delay, and the delay isvaried based on a weight of the synaptic connection and structuralconstraints of neurons or processes of the neurons the synapse connects.21. The apparatus of claim 16, wherein: at least one of each of therelational aspect neurons or each of the referencing neurons ispotentially connected to a plurality of pattern-matching neurons by aplurality of connections, the potential connection comprises a synapticconnection with a delay, and the plurality of connections have a rangeof the delays being set based on structural constraints of neurons orprocesses of the neurons the synapse connects.
 22. The apparatus ofclaim 16, wherein the one or more relational aspect neurons havedendritic processes parallel to reference neuron axonal processesallowing a range of synaptic connection parameters including variabledelay between neurons.
 23. The apparatus of claim 16, wherein the one ormore relational aspect neurons are associated with distal dendriticprocesses connecting the one or more relational aspect neurons toafferent axons with a larger delay than apical processes.
 24. Theapparatus of claim 16, wherein the one or more relational aspect neuronshave axons extending vertically to dendrites of the afferent neuronsincurring a delay to connect thereto.
 25. The apparatus of claim 16,wherein the one or more referencing neurons have cruciate dendriticprocesses that spread out to connect to multiple afferent neuron axonswith a varied delay profile.
 26. The apparatus of claim 16, wherein theone or more referencing neurons are laterally inhibited by otherreferencing neurons neighboring the one or more referencing neurons. 27.The apparatus of claim 16, wherein a delay of an input from the afferentneurons to a subset of the one or more relational aspect neurons pairedwith the afferent neurons comprises a constrained common delay.
 28. Theapparatus of claim 16, wherein feedback connections from the relationalaspect neurons to the afferent neurons are pre-synaptically inhibited byat least one of: an inhibitory feedback from neighboring afferents, aninhibitory feedback from the referencing neurons, or one or moreinter-neurons receiving an input from one or more afferents, the one ormore relational aspect neurons, or the one or more referencing neurons.29. The apparatus of claim 16, wherein a connectivity aspect between oneof the afferent neurons and one of the relational aspect neurons pairedwith that one afferent neuron is learned in a development process. 30.The apparatus of claim 29, wherein the connectivity aspect comprises adelay.
 31. An apparatus for neural component replay, comprising: meansfor referencing a pattern in a plurality of afferent neuron outputs withone or more referencing neurons; means for learning one or morerelational aspects between the pattern in the plurality of afferentneuron outputs and an output of the one or more referencing neurons withone or more relational aspect neurons using structural plasticity; andmeans for inducing one or more of the plurality of afferent neurons tooutput a substantially similar pattern as the referenced pattern by theone or more referencing neurons.
 32. The apparatus of claim 31, wherein:an unsupervised learning is used to learn at least one of the pattern,the referencing, or the relational-aspects, the unsupervised learningcomprises spike timing dependent plasticity and the structuralplasticity controlling weights, delays, and inputs of synapsesassociated with neurons, and the structural plasticity is constrained bystructural relationships between the neurons or their processescomprising at least one of a variability of delay, a range of delay,inputs, a weight range, or a weight variability.
 33. The apparatus ofclaim 31, wherein a constraint model for the structural plasticitycomprises limitations on at least one of: delays corresponding toparallel or cruciate dendritic arbors and axonal processes, inputneurons based on proximity or extent of dendritic arbors or axonalprocesses, or delays relating to basal or apical dendritic processes.34. The apparatus of claim 31, wherein: at least one of each of therelational aspect neurons or each of the referencing neurons ispotentially connected to a plurality of pattern-matching neurons, thepotential connection comprises a synaptic connection which is reusable,and the synapse is reused with a new delay based on a weight of thesynaptic connection and structural constraints of neurons or processesof the neurons the synapse connects.
 35. The apparatus of claim 31,wherein: at least one of each of the relational aspect neurons or eachof the referencing neurons is potentially connected to a plurality ofpattern-matching neurons, the potential connection comprises a synapticconnection with a variable delay, and the delay is varied based on aweight of the synaptic connection and structural constraints of neuronsor processes of the neurons the synapse connects.
 36. The apparatus ofclaim 31, wherein: at least one of each of the relational aspect neuronsor each of the referencing neurons is potentially connected to aplurality of pattern-matching neurons by a plurality of connections, thepotential connection comprises a synaptic connection with a delay, andthe plurality of connections have a range of the delays being set basedon structural constraints of neurons or processes of the neurons thesynapse connects.
 37. The apparatus of claim 31, wherein the one or morerelational aspect neurons have dendritic processes parallel to referenceneuron axonal processes allowing a range of synaptic connectionparameters including variable delay between neurons.
 38. The apparatusof claim 31, wherein the one or more relational aspect neurons areassociated with distal dendritic processes connecting the one or morerelational aspect neurons to afferent axons with a larger delay thanapical processes.
 39. The apparatus of claim 31, wherein the one or morerelational aspect neurons have axons extending vertically to dendritesof the afferent neurons incurring a delay to connect thereto.
 40. Theapparatus of claim 31, wherein the one or more referencing neurons havecruciate dendritic processes that spread out to connect to multipleafferent neuron axons with a varied delay profile.
 41. The apparatus ofclaim 31, wherein the one or more referencing neurons are laterallyinhibited by other referencing neurons neighboring the one or morereferencing neurons.
 42. The apparatus of claim 31, wherein a delay ofan input from the afferent neurons to a subset of the one or morerelational aspect neurons paired with the afferent neurons comprises aconstrained common delay.
 43. The apparatus of claim 31, whereinfeedback connections from the relational aspect neurons to the afferentneurons are pre-synaptically inhibited by at least one of: an inhibitoryfeedback from neighboring afferents, an inhibitory feedback from thereferencing neurons, or one or more inter-neurons receiving an inputfrom one or more afferents, the one or more relational aspect neurons,or the one or more referencing neurons.
 44. The apparatus of claim 31,wherein a connectivity aspect between one of the afferent neurons andone of the relational aspect neurons paired with that one afferentneuron is learned in a development process.
 45. The apparatus of claim44, wherein the connectivity aspect comprises a delay.
 46. A computerprogram product for neural component replay, comprising acomputer-readable medium comprising code for: referencing a pattern in aplurality of afferent neuron outputs with one or more referencingneurons; learning one or more relational aspects between the pattern inthe plurality of afferent neuron outputs and an output of the one ormore referencing neurons with one or more relational aspect neuronsusing structural plasticity; and inducing one or more of the pluralityof afferent neurons to output a substantially similar pattern as thereferenced pattern by the one or more referencing neurons.